AircraftVerse: A Large-Scale Multimodal Dataset of Aerial Vehicle Designs
Authors: Adam D. Cobb
Anirban Roy
Daniel Elenius
Frederick Michael Heim
Brian Swenson
Sydney Whittington
James D Walker
Theodore Bapty
Joseph Hite
Karthik Ramani
Christopher McComb
Susmit Jha
Venue: NeurIPS 2023 Datasets and Benchmarks Poster (2023)
Keywords: CAD; Multimodal; Multi-Physics; Aircrafts
TL;DR: We present AircraftVerse, a publicly available dataset with 27,714 diverse air vehicle designs. Each design consists of a design tree structure, a CAD model, and scientific flight simulation derived performance metrics.

Abstract: We present AircraftVerse, a publicly available aerial vehicle design dataset. Aircraft design encompasses different physics domains and, hence, multiple modalities of representation. The evaluation of these designs requires the use of scientific analytical and simulation models ranging from computer-aided design tools for structural and manufacturing analysis, computational fluid dynamics tools for drag and lift computation, battery models for energy estimation, and simulation models for flight control and dynamics. AircraftVerse contains $27{,}714$  diverse air vehicle designs - the largest corpus of designs with this level of complexity. Each design comprises the following artifacts: a symbolic design tree describing topology, propulsion subsystem, battery subsystem, and other design details; a STandard for the Exchange of Product (STEP) model data; a 3D CAD design using a stereolithography (STL) file format; a 3D point cloud for the shape of the design; and evaluation results from high fidelity state-of-the-art physics models that characterize performance metrics such as maximum flight distance and hover-time. We also present baseline surrogate models that use different modalities of design representation to predict design performance metrics, which we provide as part of our dataset release. Finally, we discuss the potential impact of this dataset on the use of learning in aircraft design, and more generally, in the emerging field of deep learning for scientific design. AircraftVerse is accompanied by a datasheet as suggested in the recent literature, and it is released under Creative Commons Attribution-ShareAlike (CC BY-SA) license. The dataset with baseline models are hosted at http://doi.org/10.5281/zenodo.6525446, code at https://github.com/SRI-CSL/AircraftVerse, and the dataset description at  https://uavdesignverse.onrender.com/.

Aircraft A Large-Scale Multimodal Dataset of Aerial Vehicle Designs
Adam D. Cobb;
Anirban Roy;
Daniel Elenius,
E. Michael Heim;
Brian Swenson!
Sydney Whittington!
James D. Walker!
Theodore Bapty; Joseph Hite;
Karthik Ramanij
Christopher McComb,
!
Susmit Jha"
CComputer Science Laboratory, SRI International
Mechanical Engineering Division, Southwest Research Institute
Intelligent Systems Division, Southwest Research Institute
Institute of Software Integrated Systems, Vanderbilt University
"Scoool of Mechanical Engineering, Purdue University
Department of Mechanical Engineering, Carnegie Mellon University
Abstract
We present AircraftVerse, a publicly available aerial vehicle design dataset. Air- craft design encompasses different physies domains and, hence, multiple modalities of representation. The evaluation of these cyber-physicul system (CPS) designs re- quires the use of scientific analytical and simulation models ranging from computer- aided design tools for structural and manufacturing analysis, computational fluid dynamics tools for drag and lift computation, battery models for energy estimation, and simulation models for flight control and dynamics. Aircraf terse contains 27,714 diverse air vehicle designs the largest corpus of engineering designs with this level of complexity. Each design comprises the following artifacts: symbolic design tree describing topology, propulsion subsystem, battery subsystem, and other design details; a STandard for the Exchange of Product (STEP) model data; a 3D CAD design using a stereolithograpyy (STL) file format; 3D point cloud for the shape of the design; and evaluation results from high fidelity state-of-the -fee----thhrrrr art physics models that characterize performance metrics such as maximum flight distance and hover-time. We also present bascline surrogate models that use dif- ferent modalities of design representation to predict design performance metrics, which provide part of our dataset release. Finally, discuss the potential impact of this dataset on the use of learning in aircraft design and, more generally, in CPS. Aircraf Verreftterreeee is accompanied by data card, and it is released under Creative Commons Attributionn-SaarrAikee (CC BY-SA) license. The dataset is hosted at at |tltttssaaarrrrffavveeeee.eeeeee www.:/atteeeeeeeeoooneee.ooooom
6625446 baseline models and code and the dataset description
Introduction
Datasets of complex cyber-physical systems (CPS) are difficult build and large CPS datasets are not publicly available. Their availability is limited for multiple reasons, such as: proprictary restrictions; difficulty in assembling the right mix of experts; and the slow manual design process
37th Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmarks.

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due to their complexity. However, huge opportunity exists to apply data-driven approaches to CPS once such dataset becomes widely available. Electric Vertical Take- Off and Landing (eVTOL) aircraft represent an emerging class of CPS. The use of electrical propulsion and the growing energy density of available batteries have fueled rapid growth in eVTOI. aircraft designs from food delivery/logistics [12), to the detection of sharks along highly populated beaches (α8]. and to air taxis. We expect this variation to only increase as battery technology continues to improve [Q. The diversity within the electric aerial vehicle design space is large due to the many choices available for selecting structural, mechanical, and electrical components. The multiphysics nature of CPS designs necessitate heterogeneity in representation, such as the use of stereolithographic (STL) files for computational fluid dynamics (CFD) analysis and the use of symbolic descriptions of motors and speed controllers for electrical analysis. Data-driven learning methods for CPS must be able to handle this diversity and multimodality of CPS designs. Aircraft Versse provides such a dataset to enable further research into data-driven methods for characterizing designing CPS.
SketchGraphs
ner odler oppecrett
ABC
accet deigh and
DeepCAD
(a) CAD Datasets
(b) Example of 3D CAD models of aireraft designs in AircraftVerse
Figure 1: Existing CAD datasets (SketchGraphs 28 DeepCAD
ABC [9] are focused on CAD for mechanical parts. Aircraft designs in AirsraftVerse include CAD models one of the modalities. A CAD model (STEP or STL) is an assembly of several components such as propellers, wings, connectors, beams, motors, batteries, and hubs. The CAD designs in dircraftVerse more complex compared to the existing datasets. The inset magnifications of couple of parts of one of the designs in the figure above demonstrates this complexity. In addition CAD models, each design also includes symbolic design tree with additional details such as propulsion and battery subsystems that are needed for performance analysis (e.g. electrical and flight dynamics analysis). Aircraft t terse also contains the result from the evaluation of each design using high-fidelity scientific and engineering tools. Thus, ArrraftVerss a CPS dataset and not just a CAD dataset.
55
Aircraf tVerse (Figure contains 27,714 diverse aireraft designs where each design is repre- sented in multiple modalities, including a computer aided design (CAD) model and symbolic de- scription. Each design is also accompanied with detailed evaluation results. Thus, Aircraf tVerse naturally amenable to emerging neurosymbolic and other deep learning methods for generative modeling, surrogate learning and sequence -oueenee---seeuence models. Overall, our paper is structured as follows. In Section we provide a summary of the few existing related datasets. We then introduce AircraftVeres in Section and describe the constituent parts that make up design. We highlight the potential of Aircraft teraeeeeeeeee in Section by providing an overview of the diversity of designs and by displaying experiments on surrogate modeling, while noting that this covers just a small part of what kind of experimentation is possible with this new dataset. We then conclude in Section /
2 Related work
The use of machine learning CAD has gained significant attention, and a few datasets have been proposed in recent literature to enable development and benchmarking of machine learning approaches. SketchGraphs dataset (28 a collection of sketches extracted from parametric CAD
models which begin wwo-dimensional (2D) sketches consisting of geometrie primitives (e.g., line

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segments, ares) and explicit constraints between them (e.g., coincidence, perpendicularity) that form the basis for paree-dimensional (3D) construction operations. This dataset has been used for generative
model of CAD sketches [34, and other applications of learning in physical design 29 23 Another
example of a CAD dataset that focused on physical structure is SimJEB [3), which is a dataset of crowdsourced mechanical brackets and accompanying structural simulations. DeepCAD
K55] is
a dataset of 3D shapes corresponding to objects such as flanges, pipes and screws, represented as sequence of operations used in a CAD framework to generate these shapes. Another dataset for 3D engineering shapes is the ABC dataset 191, which comprises geometric models, each defined by parametric surfaces and associated with ground truth information on the decomposition into patches. These datasets are excellent resources for their target application domains such as extrapolating 2D sketch to CAD designs, and generating mechanical parts.
In contrast, AircraftVerse is a dataset that covers the more complex design space of electric aircraft focused on system-level CPS design and thus, complements existing CAD datasets. In Figure 1 we illustrate the complexity of the aircraft designs in AircraftVerse compared to mechanical components from the existing CAD datasets. The CAD models of an aircraft design in Aircraft wings, hub and connectors. Further, the Aircraf Weerse dataset includes additional description of a design beyond its CAD model, such as its propulsion subsystem and its electrical subsystem. This is crucial for creating description that can predict the performance (flight dynamics, electrical analysis) of a design as designs with similar CAD structure can have very different performances depending the used components. We also provide the performance of each design using detailed high-fidelity physics and engineering simulation tools to enable the use of this dataset not just for generative modeling, but also for learning surrogates and design characterization and optimization.
are an assembly of several mechanical components, such as propellers, motors,
3 AircraftVerss dataset
The key characteristics of AircraftVerse are as follows:
The number of designs in AircraftVerse is 27,714 making it a uniquely large-seale CPS dataset with high design complexity. Design curation required finding valid aircraft configurations followed by detailed simulations to evaluate its performance objectives. We have attempted to create balanced datasct that includes a sufficient number of aircraft designs across a range of different flight performance metrics, such hover times and maximum flight distances. Our search for designs included over a hundred thousand candidates from which we selected these 27,714 designs ensure diversity in design and performance. Our design process itself is peerrssttprrservingg and ensures optimization does not lead to very similar designs 5 [
sircraftVerse represents design using multiple modalities that include: symbolic design tree describing the design topology, propulsion subsystem, battery subsystem, and other design details; a STandard for the Exchange Product (STEP) model that is a decomposable CAD model showing each part separately; and stereolithographic (STL) CAD model that is ideal for computational fluid dynamics analysis, and the corresponding 3D point cloud for the shape of the design.
Aircraf terse uses results from high-fidelity physics models and an evaluation pipeline developed as a part of DARPA's Symbiotic Design of CyberPhysical System's 's program to evaluate different aircraft designs. These physics models characterize performance metrics such maximum flight distance and hover-time. The evaluation pipeline uses a mixture of custom flight dynamics simulators (31 and commercial tools such Creo 24. The evaluation of design determine performances such as its drag, lift, maximum flight time and hover time requires a significant compute infrastructure and requires subject-matter expertise. We include these evaluation results as part of the dataset.
The designs in Aircraft Veese exhibit a very high degree of diversity in their topology, the choice of energy subsystem and the choice of propulsion. The designs are a mixture of rotorcrafts, winged aircrafts and hybrids with 28% capable of vertical takeoff and landing D To the best of our knowledge, there is no other available corpus of such diverse aircraft designs with the design details and the results from detailed scientific and engineering evaluation.
https::/wwwwddast www.wwwwww.mmlll...c.m. hww.ppaai///porraaasmmiiiioo-eoo forerr/biriiieesigneooooorrrrrpe We note that some aerial vehicles, such as many fixed wing aircraft, are launched (or catapulted) and therefore not required to hover.
for-cyberr-rreeee www.-cobbrr-hssiccaaaaa-e----eee pyysical-systens.
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We include multiple baseline surrogate models to predict some of the key design performance metrics. Our surrogate models include transformer encoder model and an LSTM model that take the symbolic design configuration, as its input, as well as graph convolution neural network and PointNet model that use the point cloud modality. These baseline models are used to predict the mass, the maximum flight distance, the maximum hover-time and the presence of any structural interferences that needs to be avoided for fabrication and manufacturing.
Dataset Availability. The dataset is publicty available for free under Creative Commons Attribution- ShareAlike (CC BY-SA) license, which will enable future extension and adaptation of the dataset by others. With respect to its maintainability and long-term availability, the dataset is hosted atttps/: // zenodo. wwt.:ooorggrrrreeeeeeeeeoeo org/recood//65544 and will be maintained by SRI Internationa f a non-profit research institute with over 75 years of history of contributing society and research community, with proven track record of building, maintaining and distributing several datasets such as BioCyc [T]. Voices 2 and open-source tools- some of which have been maintained for multiple decades.
The designs in AircraftVerse are comes from the electric motors that power the propellers. Our design corpus includes range of propellers, motors, batteries, and fixed wings. The list of components is provided in Appendix F We use the propulsion subsystem design an example describe the curation of the dataset. We use combination of wings and propellers (in the right topology) provides the thrust and lift needed for efficient horizontal flight and vertical take-off and landing. Our components are diverse, such the propellers have a different number of rotor blades with different diameters and pitches, the component motors have different Ku. Km. K₁ ratings, and the batteries have different peak and continuous current ratings and different voltage ratings. Consequently, the design choices allow significant diversity in structure and composition for similar performance. For example, the thrust produced by propeller-motee motor pair can be related approximately to the parameters by the following formula: Thrust x (K₂ x V oottage// (aatgee)///tchhh (Pitch x Diameter). where K, the motor's "Kilovolt" rating, which represents the number of revolutions per minute (RPM) that the motor will produce per volt, V Vollage the voltage being applied to the motor, Pitch is the distance that the propeller would move forward in one rotation, and Diameter is the distance across the propeller. Our design used more detailed models [11 2z. , but this dependency illustrates how the same thrust can be produced by different combinations of propellers and motors. High K2, rating motors that rotate much faster can produce the same thrust with smaller propellers when compared to lower K, (slower) motors with larger diameter propellers. These combinations can have different continuous and peak current requirements that would influence the battery choice. In addition to these propulsion choices, connectors such as parametric joints, hubs, and arms can be put together in fiexible ways to build interesting design geometry and topologics. These can have an impact on the experienced drag, which would then influence the requirements for propulsion. Thus, the design of aircraft requires making number of tightly coupled design choices. The performance of each design can be assessed using a pipeline comprising commercial tools such Creo 24! and custom flight dynamics model (FDM) 33 2 Each aircraft is also assessed on controllability (existence of trim states where the aircraft remains stable in the absence of environment perturbations) at different speeds using the FDM. In particular, the FDM evaluates whether the aerial vehicle can fly at a specific velocity through optimization schemes that check if the translational and rotational accelerations can be driven to zero for a given design during vertical take-off and horizontal flight (more details in Appendix C Manually finding a large number of feasible designs is very challenging and prohibitively time-consuming. Instead, we use a learning- based design approach [6]. We create a number of designs using a procedural aircraft generator that uses heuristics provided by domain experts, such as limiting the complexity of produced designs to have at most 16 propellers and at most 12 wings, using design motifs and symmetries that help with controllability of the aireraft. A transformer-based model is trained on designs with good flight characteristics to act as filter in the future generation process (Appendix B ()). Each of the final 27,714 designs included in the dataset is run through the detailed scientific and engineering models 31 characteristics.
Dataset Curation.
where the propulsion
2 24 to generate the metadata describing the design performance and
Design Structure and Representation. Each design in the dataset consists of the following
Phups://en. http:/awwwgwwwi//SRinttntnnee
' "htpss//gituu..oom/SS com/SRI-CSI. For a complete printout Appendix G

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Figure 2: An illustration of all the key file components that make up an aircraft design. The design_tree.jsen captures all aspects of the design (e.g. 4-way hub) that appears in both the cadfile.stl and the Geom. stp. Additional files of besign_seq.jes design_tree mesigg_tree.. json: The design tree describes the design topology, choice of propulsion and energy subsystems. The tree also contains continuous parameters such as wing span, wing chord, and the lengths of propeller arms and connectors. In our dataset, we also include preorder traversal of the design tree and store this as design_ Bei.in_sse____eee see json to facilitate the use of sequence- friendly model architectures. In addition, we include desigg Baw_gg__wwww_____ sgn_low_eeeel__ _evelleeel.__ json, which is more fine-grain gre-grained engineering representation of the design. This fine- fine-grained representation includes significant repetition that is avoided in the abstract tree representation through the use of symmetry (such as specifying that the same arm structure is repeated six times around a hub). See the pictorial representation of the design tree in Figure 2 that demonstrates a simple example of such symmetry.
Geom stp, cadfile. stl, and pointCloud .ny: CAD design for the acrial vehicle in com- positional STEP format (ISO 10303 standard), its stereolithograpii STL file, and a generated pointcloud of 10,000 points from the CAD representation.
output. json and trims.mpp mpy: Summary files containing the vehicle's s performance metrics such maximum flight distance, maximum hover time, flight distance at maximum speed, maximum current draw, and mass. The trims. npy contains the [Distance, Flight Time, Pitch, Control Input, Thrust, Lift, Drag, Current, Power] each evaluated trim state (velocity) of the aircraft. Designs that have more trim states and with contiguous trim states are preferable as these designs would be relatively easier control even when the environment is noisy. Thus, both the output. json and trims. npy contain the evaluation of the performance of each design. See Figure 2 for an example of these evaluation indicators.
design_tree tree. json:
cadf stl::
output.json
Connected 4-way Sym. Hub
Geom stp:
"Interferencee"
"Kase": 3,61,
"Batt_caps_zae" Mx3t:o_.
Propeller Arm
Fuselage
"Distance_Mudee 442.37,
pointCloud, mpy:
"Kat_Diatance" :50
"Rover_Tias" :31.22
"Kax "Kax_Speed" Spt.Spped""11.000
Battery
pot__aps_rattoo_ee
Motor
Propeller
Flange
"Kot_anpe_ratiooN..34
trims npy:
"Kot_power_ration wtwp:rr__tttooooo00...
"Kot_pover_ratta" www.oorr_rrttooo.ttpp".000
"Fover NFDD::644.00"
1299
"Fover_Mrrpp"" 1617.85,
"Speed_ 22D"
Tea 1m50
Tux
Tie JAN
and jaon are directly derived from the design tree. The performance the aerial vehicle is displayed in both the performance file labeled output. json and in the trims. npy.
Design Example. The design tree used for aireraft designs reflect the hierarchical nature of CPS designs. Figure 2 shows an example design. It has a 4-way central hub component, Each of the four arms of the hub have propulsion subsystem attached to it. We identify this subsystem MainSegment which can be implemented via different choices (subtrees expanding MainSegment) such as BendSegment, PropArm, ExtendedProoAAme WingArm, and so on. Each of these choices describe the nature of the propulsion subsystem, such as whether we are using propeller or wing, and whether the arm making connection is a simple connector or composition of connectors. Further, some of these subsystems can be recursive, allowing fractal growth of the design. For example, an arm itself might contain another segment with hub having number of arms. Our symbolic representation of design also enables exploitation of symmetry to compress the design description, and inclusion of expert knowledge the design. For example, we want four arms of hub to contain the same subsystem, then we only need to specify it once (as single child in the design tree) and use a symmetry tag over the hub, for example, ConnectedHubs Sym denotes hub with four connections each having the same subsystem. In the case of Figure 2 a propulsion subsystem
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Hover Time: 52.5
Hover Time: 648.2
Hover Time: 144.3 Flight Dist.: 2456.2 2
Hover Time: 72.2 Flight Dist.: 1036.2
Flight Dist.: 255.2
Flight Dist.: 13105.2
Hover Time: 0.0 Flight Dist.; 1924.0
Hover Time: 0.0 Flight Dist.: 4088.0
Hover Time: 0.0 Flight Dist.: 713.4
Hover Time: 60.8
Flight Dist.: 1341.8
Hover Time: 85.7 Flight Dist.: 1162.9
Hover Time: 30.0 Flight Dist.: 87.9
Hover Time: 337.0 Flight Dist.: 8273.8
Hover Time: 573.4 Flight Dist.: 10943.6
Hover Time: 99.9 Flight Dist.: 1944.9
Hover Time: 92.6 Flight Dist.: 1818.8
Hover Time: 75.8 Flight Dist.: 1581.9
Hover Time: 36.9
Flight Dist.: 437.0
Figure 3: Diverse vehicle designs in AircraftVerse with different structures and with different performances. Some designs can hover in place. Hover time is in seconds and flight distance is in meters.
comprises a propeller, motor, and a flange. Further, the hub also connects to a fuselage that contains battery subsystem that can consist of single or dual battery subsystem along with additional electronics and their position within the fuselage. An explicit example of this is provided in Appendix Additionally, the full corpus of options included in Appendix F is also provided Python dictionary as part of AircraftVerse
G
Design evaluation. For each design, we use state-of-the thetth-rrttttt tools 24 to compute the physical characteristics, such as the mass and the component interferences, as well as to render the 3D CAD models (STEP and STL files that are included in AircraftVerse to the FDM B for evaluating flight dynamics (more details in Appendix C The dynamics model considers the aircraft velocity and its roll, pitch and yaw rotations, along with the electrical state such as the power drawn from the battery and motor current, which turn determine the torque on the propeller and its generated thrust. The dynamics model summarizes the impact of different forces such as gravity, drag, propeller lift and wing lift, and includes scientific models for conversion of electrical energy into mechanical energy in the propulsion subsystem. From these evaluations, we include Batt ___pp___________________________ amps_ratio ratio_MFD, Batt Batt_amps rut_ratioo___ ___S_p___ppp that correspond to the ratio of maximum current drawn by the design compared the maximum current rating of the battery maximum flight distance and maximum speed, respectively. These indicate the robustness of the bat- tery subsystem of the design. We also include Mot voo__ams____ tat_aaasssaiiio srtss_rat_______ee __D, amps_mpp____ smps_rrata araatioo __M_ ppower_ratii Beatio__Mart __MXppd that represent the ratio of the current/power
These details are then passed
Hot_power: cacerrrrtto___ Aesio_MFy MFD, in the motor divided by the maximum allowed current/power corresponding to the maximum flight distance or maximum speed of the vehicle. These indicate the robustness of the propulsion subsystem of the design, its endurance, and its agility. Finally, we include design metries such as the flight distance at maximum speed (Distance 3 maximum flight distance (Max Distance), and the maximum hover Snover_Timel
4 Experiments: diversity, multimodality and baseline models
Diversity. AircraftVerse includes a diverse set of aircraft designs (samples shown in Figure B and the multimodal representation makes this dataset relevant variety challenge problems in machine learning. The components of design in Aircraft Verse are selected from a large component corpus with hundreds of choices of propellers and motors (Appendix F). addition, each discrete choice of component comes with its own of attributes that fall in their own ranges (c.g.

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UAV Designs
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Figure 4: The designs in AircraftVerse exhibit diverse performance with respect characteristics such as maximum flight distance and hover-time. They also have very different physical characteristics such mass, number of propellers, and number of wings.
motors have a large set of parameters such as motor armature- resistance and inductance, mass, cuuner--lectrromotiv force constant, and motor torque constant). Adding to the overall complexity, there are also the continuous parameters. For example, structural components such as connectors have their radius and length as continuous parameters. In Appendix F we identify the attributes of the key components an aerial vehicle design and whether their values are fixed or variable within range. Some characteristics of the design diversity are captured in Figure 4 The figure highlights some of our curation choices. First, the majority of designs have an even number of propellers due to the symmetry built into tree structure definition. Second, designs without propellers and favors designs with fewer wings. Finally, while a significant number of designs can take off and achieve horizontal flight (with large variation in ability), one of the challenges building a diverse dataset comes at the expense of many designs failing to have the hovering capability other e eVOL flight performance metrics. However, these designs useful for learning potential rules as to why these designs have poor characteristics compared to others, so also include these in the dataset.
dataset precludes
While structural differences lead to visually striking diversity, we also highlight that design diversity has other aspects, such as the use of different components within the same design topology. Vehicle designs with the same topology can perform very differently. Figure 5 contains four symmetric quadcopters with the same topology (same design tree structure). However, their performance metrics differ significantly, with the maximum flight distance differing by a factor of over 2x. Additionally, the electronics of the rightmost quadcopter varies from the other three in that it has two batteries rather than one. Thus, the performance of designs with similar topologies cannot be predicted from just from the CAD design, but requires the symbolic design description in design_ gree...eeeeeeeeeeeeeeeeeeeeeee json.
Hover Time (s): 31.2 Flight Dist. (m): 505.2
Hover Time (s): 67.5 Flight Dist. (m): 1329.8
Hover Time (s): 83.9 Flight Dist. (m): 1268.4
Hover Time (s): 56.2 Flight Dist. (m): 880.2
Propeller: 1866
Propeller: 9x6
Propeller: 18x8
Propeller: 16xt
Motor: AT4125KV3440
Motor: KDE600XF-1100 110XXXFF--00----
Motar: All 3530K580
Motor: AT4120KVV50
Baltery: 1000mAh 7SC
Battery: m200
Battery: 2200mAh 7SC
Battery: 1000mAh 75C
Figure 5: Diverse aerial vehicle designs in Aircraft Verse with same topology structure but different motor/propellerbbtteer choices leading diverse design performance.
Multimodality. The AircraftVerse uses multiple modalities to represent an aircraft design, as illus- trated earlier in Figure 2 We would not expect the performance of electric acrial vehicle designs to be
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accurately predicted on the basis of only one of the modalities (e.g. the 3D CAD model). Recent work on vision-language models BIS
7 would be especially relevant large CPS datasets such as AircraftVerse. However unlike in the vision-language scenario, AircraftVerse has stricter metadata that enforces physical and electrical constraints. The multi-modality of Exeraffterre makes it applicable to a wide class of machine learning models. AircraftVerse includes the following modalities:
Structured sequential data: The success of transformer-basee sequence models used in natural language 80
3 21 has driven their adoption and extension to structured data such as computer programs. The sequential data from the design trees in Aircraft is interesting use case for transformer models. Further, the designs in dircraftVerse are more structured than natural language text as the design components need satisfy physical and electrical constraints
9 26
There is growing interest in using machine learning for CAD design [28 The 3D CAD models (STL and STEP files) in AircraftVerse provide a large dataset with complex 3D shapes. The metadata associated with the designs can be used to train and evaluate methods that predict the physics metrics from the CAD models.
3D CAD models:
23 33
331
19
3D Pointcloud: The use of machine learning for learning generative models or manipulating or segmenting 3D point clouds has also received significant attention. Aircraf t Verse includes 3D> point clouds extracted from the STL and STEP files. Thus, this dataset with the pointclouds can be used benchmark for 3D point cloud classification (e.g.. presence or absence of interference) and regression (c.g., predicting flight characteristics).
36
33
Baseline Models. We describe baseline models applied multiple modalities of Aircraf terse We use a transformer encoder (T. Enc.) 60 model and a LSTM 4 model to predict a set of design performance characteristics of an aircraft from its symbolic design tree description. In our example, even using these symbolic descriptions of a design contain information from multiple modalities, as these sequences include electrical, mechanical, and topological information. We also test out the 3D modality of the design representation by applying both a graph convolutional neural network (GCNN)
17 and PointNet (25) to the point clouds. For the sequences, we use the design representation in design_seq. json and use our cestom-buille tokenizer to convert each token of design sequence from the symbolic representation (c.g. { ('node type': "ConnectedHub3 1auuuuu____11___ )') into a tensor. For our embedding, we assign one-hot encoding for the keys and an additional one-hot encoding for the values. For keys that require floats as values (e.g. 'armLength') we ensure that there is a class that corresponds to the float in the value embedding, as well as appending the value of the float to the tensor. Each token of the sequence is a 749-element vector corresponding to the concatenation of 43 classes of keys, 673 classes of values, 32 possible attributes of electrical/meccanical components, and a final dimension for float values. We provide our tokenization code as part of the dataset release, as well as the baseline models. Further architectural details of the models are presented in Appendix D
Results. The results of predicting the performance statisties from the design sequences are presented in Table [ This table shows how the sequential design description, containing specific component information such as mass and electrical information, useful modality for certain metrics such as the ability of design to fly. However, both the baseline GCNN model and PointNet are better at inferring structural interferences between components as it directly incorporates 3D information. Future approaches that combine the 3D structural information and symbolic design descriptions may be necessary and we have demonstrated here that the AircraftVerse dataset includes all of these informative modalities. Figure O shows both the receiver operator characteristic (ROC) curve and precision-recet recall curve for the three models. Again, highlighting the superior performance of the sequential information in estimating the high level CPS performance metrics such as ability to hover.
5 Limitations of AircraftVerse
While Aircrafteerse presents itself as a unique CPS dataset with a high level of complexity, there are several limitations using this dataset. One limitation that aireraft designs are constrained to fit within the tree-structured representation. This tree structure currently means all designs start from sampled single hub and are then expanded. This process described in fntttt:////// //tp::///tthb
See Appendix for a definition of the structure of our design trees and sequences.
OOu models were trained using NVIDIA 100 SXM4 80GB GPU.

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True Positive Rate
ROC Curve for Classification of Hover Time
PR Curve for Classification of Hover Time >
1.0
1.0
Transformrr Fsc: (AIC 0.961
Precision
0.8
Transfomer Fnc.
0.6
LSTT LNUC 0.951
LSTM
0.4
GCNN
0.6
(AUC
PointNcc
0.2
Random Clasiiier
0.4
Ranoom Classifer
0.00
0.2
0.6
0.8
0.0
0.2
0.4
0.6
0.8
1.0
False Positive Rate
Recall
(a) ROC curve
(b) Precision-Recall
Figure 6: ROC and precision-rectt curves for predicting design's 's ability to hover. These results suggest that the symbolic design description in AircraftVerse can be used predict flight charac- teristics, whereas Table [ highlights the utility of point clouds for predicting structural information.
Table Specification prediction using baseline models.
APPROACH
DATA
HOVER
HOVER
FLIGHT
INTERFERENCE
TIME (R² t)
DISTANCE (R² t)
(Acc: t)
(R² 1)
(FI ScoRE f)
T. EXC. LSTM GCNN
SEQUENCE
0.9004
0.6778
0.9964
0.6900
0.8270
SEQUENCE
0.8838
0.6189
0.9974
0.3467
0.8184
POINT CLOUD
0.8350
0.3936
0.8888
0.4486
0.9758
POINTNET
POINT CLOUD
0.8-171
0.4571
0.9212
0.5099
0.98-18
which also includes all incorporated prior domain knowledge. Designs are also limited by the available components in the database. For example, hybrid (gas and electric) vehicles are not possible. However, all of the database components are from real-world catalogues which increases the overall value of the dataset.
com/ CESRI-CSL/AirCCE Cataa////AA.ccaaattee/eeeeeee//e
As with all scientific simulators, the flight dynamics model is limited in its ability to represent all aspects of the real-world. As such, there are likely corner cases that would cause unrealistic behaviors. One weakness of the simulator is in the drag model described in Equation (Appendix C The drag model approximates the total surface area in each axis and therefore would be superseded by higher fidelity CFD software. However, the simulator uses conservative estimates for such calculations and has been compared to higher fidelity models to ensure they capture the correct behavior. The results are therefore still useful for design prototyping purposes. further extension to the simulator to increase fidelity would also be to model material properties such as stress and strain during flight. Further development will continue to address these simulation gaps.
Given these limitations we still highlight the value of this dataset as useful for machine learning researchers looking explore a novel CPS dataset as well domain experts looking explore the potential of data-driven approaches for design. This is precisely what we found to be the case throughout the collaboration that led this work. As a result, we believe that ELrcrafteerso sufficiently complex and interesting for both sets of researchers, who would see value in the data, while ensuring that they take into account all the limitations.
Ethics Statement
The data included in the Aircraft dataset does not include any personal or sensitive informa- tion. It is a technical dataset of air vehicle designs, and thus it does not raise any direct ethical issues regarding privacy data protection. The dataset is intended for academic and research use and is released under a Creative Commons Attrbutioon-SharrAliik (CC BY-SA) license. This license allows for sharing, copying, distributing, and transmitting the work, well as adapting the work and making commercial use of the work under the condition that appropriate credit is given, a link to the license is provided, and any changes made indicated. This licensing framework is intended promote open access to information and collaboration in the scientific community, while still recognizing and respecting the intellectual property rights of the creators.
Given the potential wide-ranging impact of this dataset on the use of learning in aircraft design and other CPS, it is crucial that future research using this dataset is conducted with careful consideration

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of potential ethical implications. This includes not only the avoidance of misuse but also consideration of environmental impacts, safety, and other societal implications. We welcome community input on the dataset, its uses, and its potential ethical implications. We want to promote responsible use and want address potential ethical concerns proactively.
7 Conclusion
We have introduced a new multi-modal CPS dataset that will enable researchers to explore a new inter- disciplinary area of application for machine learning. We emphasize the richness of dircraftVerse where we provide 27,714 14 aircraft designs, each with associated metadata summarizing the perfor- mance of the design. We highlight that our experiments and baseline models have only touched the surface of the different approaches and problems that can be explored with this dataset due to the availability of all the metadata associated with each design. In addition to the material pre- sented here, we also include an extensive supplementary materials section as well as a website: https://irccafttvee www.tteereee.e.eee.ee.eeeeeeeeee onrender com/ GitHub repo: https:///githb. cww.coom///oo// SRI CSL/ AircraftVerse/ and the dataset release at btttps://zenode org/record/6625440
Acknowledgments and Disclosure of Funding
This material is based upon work supported by the United States Air Force and DARPA under Contract No. 1A8750-20-C-000 expressed in this material are those of the author(s) and do not necessarily reflect the views of the United States Air Force and DARPA.
Any opinions, findings and conclusions or recommendations
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A
Data Card
In this section we include the data card that follows the structure in Pang and Lee 22 as highlighted in Gebru et al.
A.1 Motivation
For what purpose was the dataset created? The dataset was created to enable research on wanted to see if it was possible to predict
designing eyber physical systems. In particular, characteristics of aircraft designs, and be able to filter out poor performing designs before needing to expensive scientific models. In order to achieve this, we needed to build large representative dataset to train our models.
Who created the dataset? The dataset was created by the authors: Adam D. Cobb, Anirban Roy, Daniel Hlenius, and Susmit Jha, at SRI International: Brian Swenson, Sydney Whittington, and James Walker at Southwest Research Institute; Theodore Bapty, and Joseph Hite at Vanderbilt University; Karthik Ramani at Purdue University; Christopher McComb at Carnegie Mellon University; supported by DARPA under the Symbiotic Design for Cyber-Physieee Systems program with contract FA8750- 20-C-0002.
Who funded the creation of the dataset? DARPA under the Symbiotic Design for Cyber-Physical Systems program with contract TA8750-20-C-0002.
A.2
Composition
What do the instances that comprise the dataset represent? The instances are aerial vehicle designs generated by and evaluated scientific models 2 31. We also provide metadata
in the form of the corpus dictionary such that the attributes of the propellers, motors, batteries, and wings available.
How many instances are there in total There are 27,714 instances in total.
Does the dataset contain possible instances or is it a sample of instances from a larger set? The dataset is sample of instances that were generated according to our grammar. As our generator includes both discrete and continuous parameters, is not possible to capture all possible instances of the designs. Furthermore, designs are allowed to contain recursively generated components, such as arms, such that all possible topologies cannot be sampled. The plots in Figure show the diversity in designs.
What does each instance consist of? Each instance consists of a design tree, an interpreted design sequence, a low level design sequence, an STL file (3D model), npy file containing an array of trims, npy file containing point cloud, and JSON file reporting flight statistics. Please refer to
Is there a label or target associated with each instance? The labels are given via the jesputjjson and trims. .py files. These files contain labels such whether design has in- terferences and if they are able to fly. is also possible to use the design tree or design sequence to label the 3D data. For example the low level design sequence can be used to label how many propellers are in the aircraft design or to predict which battery has been selected based on the performance file.
Is any information missing from individual instances? Everything is included. No data is missing.
Are relationships between individual instances made explicit? Individual instances are sampled independently from our generator and are therefore not explicitly related. The corpus dictionary can also be used determine the attributes of the available components where appropriate, where this corpus is relevant to all designs.
13

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Are there recommended data splits (e.g., training, development/validation testing)? In the note- book code we provide our training, validation, and test splits but they are not explicitly recommended as the data has multiple use-cases.
Are there any errors, sources of noise, or redundancies the dataset? The only errors come from the available fidelity of the scientific models. Please refer to 2 31 for the limitations of the models. As result, some of the outputs from the metadata contain NaNs. These are simple enough to filter out from the dataset where needed.
Is the dataset self-contained, or does it link to or otherwise rely on external resources? The dataset is entirely self-contained.
Does the dataset contain data that might be considered confidential? No.
Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety? Not that the authors are aware of.
Does the dataset identify any subpopulations (e.g., by age, gender)? Not applicable.
Is it possible to identify individuals (i.e., one or more natural persons), either directly or indirectly (i.e., in combination with other data) from the dataset? Not applicable.
Does the dataset contain data that might be considered sensitive in any way? No.
A.3 Collection Process
How was the data associated with each instance acquired? The data was generated by sampling probabilistic design generator and then compiling each design into format that was suitable for the scientific models. The scientific models provided performance metrics which we then curated into the output, json files and the STL files.
What mechanisms or procedures were used to collect the data? Each design took approximately 3 minutes to evaluate. As result estimate that took almost 60 days of compute power to build AircraftVerse. As part of the pipeline, we required access to CREO license
24
If the dataset a sample from larger set, what was the sampling strategy? The dataset was sampled from our probabilistic design generator and was therefore stochastic process.
Over what timeframe was the data collected? The data was generated using four compute nodes and therefore took over 2 weeks to generate. However, this was part of process that took around two years of development to get to. Part that process was building domain knowledge across the collaboration and building up the right compute architecture.
Were any ethical review processes conducted? Not applicable.
Did you collect the data from the individuals in question directly, obtain it via third parties or other sources (e.g., websites)? Not applicable.
Were the individuals in question notified about the data collection? Not applicable.
Did the individuals in question consent to the collection and use of their data? Not applicable.
If consent was obtained, were the consenting individuals provided with a mechanism revoke their consent in the future or for certain uses? Not applicable.
Has an analysis of the potential impact the dataset and its use on data subjects been con- ducted? Not applicable.
14

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A.4 Prerooesiingddemigglbllngg
of the data done? For the machine learning models, we processed the design trees into unique key-value sequence as described in Section $ As part of evaluating the performance of each design, we recorded the performance metries in the output. json file and the trims. npy file. We also used combination of data-driven heuristic approaches and hard rules to remove certain designs from the final dataset. We limited designs wings and 16 propellers. We further added data-driven preprocessing step to remove designs that would definitely not hover. We were careful to select a threshold that did not sacrifice diversity.
Was any prepocessing/deaninglalbiiii
maximum of 12
Was the "raw" data saved in addition to the preprocesed//deened/bbae contains the raw data well the processed sequences. We consider the design trees, the STL files and the performance files to be raw data.
data Each design
Is the software that was used to preprocess/dean/laell the data available? The software required to label the data requires CREO license along with proprictary software owned by Southwest Research Institute. It is possible that the latter software will be released in the future.
A.5
Uses
Has the dataset been used for any tasks already? At the time of publication, the only use-case of our dataset can be seen in Section
Is there repository that links to any or all papers systems that use the dataset? Not applicable at this time as this the first instance of the data release.
What (other) tasks could the dataset be used for? The dataset could be used for anything related modeling or understanding the behavior of aircraft. For example one could envision learning rules for good designs, inverting the design by predicting sequences from point clouds etc... Overall the Aircraf terse dataset contains multiple modalities and there are many opportunities to test new models out. We take some time in the paper to highlight this.
Is there anything about the composition of the dataset or the way it was collected and prepro- cessed/cleaned/Jabeeel that might impact future uses? The fidelity of the scientific models are the main factor for users of this dataset consider when using this data set for future applications.
Are there tasks for which the dataset should not be used? Not that the authors are aware of.
A.6 Distribution
Will the dataset be distributed to third parties outside of the entity on behalf of which the dataset was created? Yes, the dataset is publicly available.
How will the dataset will be distributed? The dataset is hosted at jattps///eendoo arg/record/6625446 baseline models and code are at httpp:://githbb. com/SRI-CSL AircraftVerse, and the dataset description is at
onrender com/
When will the dataset be distributed?
The dataset was first released in 2023.
Will the dataset be distributed under a copyright or other intellectual property (IP) license, and/or under applicable terms use (ToU)? See Section
Have any third parties imposed Ia-bd or other restrictions on the data associated with the instances? No.
A.7 Maintenance
Who will be suporriignoostin/maaaaaiinn the dataset? The Neuro-Symbolic Computing and Intelligence (NuSCI) Research Group at SRI International will support and maintain the dataset.
15

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How can the ovner/curator/mmanger of the dataset be contacted? Please see the email addresses the start of the paper.
Will the dataset be updated? designs.
We intend to continue to add to the dataset as we evaluate further
If others want to extend/augment/buill on/contribute to the dataset, is there mechanism for them to do so? Please contact the authors in regards to ways that the dataset could be extended. For example running different scientific models may be possible.
B Licensing description
Our dataset uses a Creative Commons Cerrbution-ShaaeAliiee (CC BY-SA) license. This means that the data can be shared and/or adapted for any purpose under the condition that appropriate credit is given, and that link to the license is provided, and that users indicate any changes were made. For more details on the license, please refer to https: ttttp:://creeatiecoommot wwwpaas.ooggeggeeeeeeeeeeee ptrg/iiceseesbyy// 0/
C Flight dynamics model
In this section, we describe the physics of the flight dynamics model (FDM). The vehicle is equipped with five major components 1) propellers, 2) motors, 3) batteries power source, 4) wings and stabilizers, 5) body structure such as a fuselage or a board where components are connected to. The FDM considers six-degrees of freedom where the state of the vehicle at time / defined follows
x = (U, V, W, P. Q, R, q, x, 94 Q2).
(1)
(U, V. W) is the velocity of the vehicle in the body frame along X, Y. and axis respec- tively.
(P, Q, I?) is the rotation of the body, i.e., representing roll, pitch, and yaw respectively.
q is a four-dimensional vector connecting the world frame to body frame.
(xr y, 2) the location in the world frame.
22 is the rotation for the ith motor expressed as radian/sec.
. Qj is the power drawn from jth battery.
The flight dynamics is governed by the following equation
dæ
f(x, u),
dt
where is the current state of the vehicle and u is a vector representing control signals. Each component can be controlled independently by the corresponding control signal and we assume the control signals are within range of O and 1. The function f() represents the computation of forces. The computation of various forces as described below.
Gravity. Let us consider the velocity at the world frame is v and the body frame is V, respectively. The translation matrix connective V is defined R(q). Thus, V = R(q)v. The gravitation force is applied on the the center of mass of the vehicle and computed as
Fg mg mgg)(
where 2 = (0, 0, 1) is the unit vector pointing down in the world frame.
Drag. The drag force on the vehicle's body is computed based on the vehicle's flight through the air. We assume that the velocity is the vehicle's velocity in the air. Thus, the velocity is updated from the body frame to account for the motion of the air being in the world frame U R(q) Dwe, where U is the wind velocity. Then the body drag is approximated as
Xbody ua U + 20 (U + 20m)) Y Yoddy,vel V + 2V (V / + 2V).) Z Wood + 2W in (W + 2W..). ...)
1
F3
2.
(4)
16

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where Xbody,ue uu. Ybody.ev, Zbody, wew uw are the constants for each coordinate axis and for the vehicle we set equal to a constant times the presented area of the vehicle's body along the coordinate axis. Uin; Von; Win are the wind velocity along X, Y, and Z. directions, respectively. As for the asymmetrical vehicles, center of drag may not align with the center of mass, this generates moment force given as M (X X cm) x Fb, where X ebooy is the center of body force and X center of mass.
Propeller and motor modeling. We define the geometric center of a propeller in the body frame is Xº and the unit normal vector to the propeller shaft ng. The velocity of the air passing through the stationary propeller is estimtaed as
V = (V + x (Xp- - -xmm) ne..
(5)
For propeller performance, we refer to pre-computed tables for each propeller providing performance data with respect to spin rate and advance ratio J. The spin rate defined by the rotations per second and denoted by n 92/2x. The advanced ratio is computed as J = Vpp (nD(. where D is the diameter of the propeller. The thrust and power of the propeller are estimated as
Pp = Cr(n, pm
2 DA, P = Cp (n, J) D2;
(6)
where Fp and P are thrust and power. Cr() and CpO) are pre-computed performance values depending on and J. The total force from the propeller is computed by summing over all propellers.
The torque from the motor is estimated as Tmasto Kr(1 Io), where / is the current, To is the idle current, and Kr is the torque constant. The relation between the current and voltage is given by
Ω
Vmotoo
VbatteryUu
+ IR,
Ky
where Vmntoo is the voltage applied to the motor, Viattery is the battery voltage, and ue = [0, 1] is the control signal driving the motor. Ky is the RPM constant and Rw is the resistance.
Note that the flight dynamics simulation model currently under construction, and there are some assumptions that would affect the flight characteristics and performance of a given design outside of this particular FDM. For example, the parametric connectors are currently made of 3d ABS plastic which accurately reflects the mass properties of such material, but may not hold up to the structural loads experienced during flight, which are not currently modeled. Further development will continue to address these simulation gaps.
D Additional experiment details
For the baseline experiments listed in this paper, we randomly shuffle the entire dataset and then split the data into train, validation, and test splits of 70 %, 10 %, and 20 % respectively. For the regression tasks, normalize the data using the mean and standard deviation of the data. Where necessary remove anomalous data points. For the mass prediction, we remove designs with a mass greater than 35 Kg from the data set. When report the results in for MSE, use the unnormalized values.
D.I LSTM architecture
The LSTM baseline model consists of a linear input layer followed by an LSTM layer (nn LSTM in Py PyTorch) with an input size of 512, a hidden size of 512. The LSTM layer is then connected to a final linear layer, via a dropout layer with value of 0.5, that takes in all the outputs of the LSTM layer and provides the one-dimensional prediction. During training we use the PyTorch in-built SGD optimizer with learning rate of 0.05 and set the number of epochs to 4000, where we the model with the best validation loss.
D.2 Transformer encoder architecture
The transformer encoder consists of a linear encoding layer followed by positional encoding layer. The output of the positional encoding is then passed through a transformer encoding layer (nn. Transf fonnsffrrerEEcoeer in PyTorch) with 8 layers and 2 heads. The final token from the trans- former encoder is then passed through linear layer that goes from the embedding dimension of 200 to single output dimension. During training we use the PyTorch in-built SGD optimizer with learning rate of 0.1 and set the number of epochs to 4000, where we save the model with the best validation loss.
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D.3 Graph convolutional neural network
Apart from the aforementioned models on sequence data, we also consider model that directly operates on the point cloud. We represent the point clouds as a set of 3D points and apply graph convolutional network (GCN) I7 B2 to make predictions on them. We build the graphs with the points as nodes where nodes are represented by their 3D coordinate. Fach node is connected to K nearest neighbors (K==0).. Note that the GCN model captures the structural information from the point clouds and does not have access to the specification of the components. Our GCN has four convolutional layers followed by fully connected layer. We train the GCN with Adam optimizer for 200 epochs.
E
Automated data generation
As part of automating our data generation pipeline for AircraftVerse, we defined a design grammar. Building grammar to define CPS requires significant domain expertise and Aircraf was no exception. Our focus was to build a procedural generator of designs that produced topologically valid aircraft designs, i.e. designs that the requirements needed to be evaluated by the scientific models. Appendix
G shows an example of a valid design tree. Once we built a valid procedural generator (with expert-guides distributions over all choices), we added the following heuristics: limit designs to having maximum of 12 wings: limit designs to having maximum of 16 propellers; set the probability of recursive structure be 0.25, such that do get infinitely long recursive structures. We then generated half our dataset using our procedural generator. Thereafter, we trained a cunsformer-basee classifier identify designs from their sequences that were extremely unlikely to hover (similar to the benchmark outlined in Section D The next stage of generation then used this classifier as weak filter (calibrated according to recall of 90 %) to increase the number of hovering vehicles in the dataset. Components of this process are highlighted in our previous technical report (.. Overall this process an accumulation of over two years of work in building the right domain knowledge and compute infrastructure to ensure that the data remains diverse, interesting, and useful.
F Aircraft grammar and components list
The full JSON schema for the aircraft designs in our design_tree. json files is available at https //github. www.commmc.s....eee
S/IS/RRIIIIAAAIES Sell/Airrrrffteeere wwteerseebloommainn/eeee wlwb/maan//chemaa////aaaaaaaaaae uav http//aaaasaaaaaaaaaaaeaa schema. json As men-
tioned previously, the sequentialized designs in design_segg - json files are pre-order traversals of the design trees (they be viewed sequences of key-value pairs).
Below, we also include the list of allowed values for each property (or "key") in the trees/sequences. Note that the JSON schema does not specifyy the allowed values for the component selection properties like propType, motorType, etc, as the specific lists components are subject to change, and not considered part of the design language itself. We list all the currently used values for those properties below.
Table 2: Key-value descriptions.
KEY
DESCRIPTION
VALUE OPTIONS
node_type
Main strictural components of air- like the cearmal and nector amms.
"Ang lndProprr'' 'AngladKingAm' 'BendSagrant', 'Branchli AdTogSagreet_Aayss CGsieett@B..2__yye_..e
"BrenchSegnwntt_Aaae
"Comectedilsn3_2.. 2_11" "Camettecinbd_2.22 $Comecteellobd
2oomectedilibd_1123 1_,___1, "Damectaddlile_22222 2_2,,,
"Commecsadiubsi_.e _By'',
"Double BoodbleBundSgganntt 'Peophrn", "SideaaysBanBBgwwea.,
Sxseewyylleccitttiogggggemm "Vanghrn".
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18

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Table 2 confinued from previous page
KEY
DESCRIPTION
VALUE OPTIONS
proptype
Propeller type.
pro.ppoollerss 10x6', "
ppp__ppopllerr_too 10x7""
"sps_progellers__
1ia_eerr____x___... 11x5_5',
"gpc.propellerr 11x0',
"app wttp.poppeeers..aa 12x112"
*sp_.propellers_12xx0
ispc.progeilere.
pepc.poopeleerr_1100
-.pc_propelier__111
"ape. www.proopoleeaiiB.5
"ap propollers.13...
spp_prropeller__2555
13x8",
wwe.propeelere_tte 24x7",
lape propeeleerr..cote 10x8_6"
"eps_progelieaat5a
peg..propellara. 16x10",
"spo.progeIiero_1Gaxx
apc_peopellerr_eBee
'apc apc.propellers_tt 18x8"
"apc.
g.ee.propeleere_200
Kapc. "spc_prapeleers_6 9__,oorr_________.
"epc_propuulerr__ 2_ 5_6xx_5"
*spc_progeIerr_502
'apc_pregeilera,
_gg_propellers_gu
-sp..propellers..xx..
'spc_progellere" 6x6",
"apc.propellerr.fgg
Spp_.propeliera7745
"ape. pppppropelles_77
'sps_propetiera_BeG
apc.propellere.ee 1x 9x
"ape apc_propellers_ee
notorType
Motor tope.
"Idd. [axrett_0622000F2150 "ado, direct X0621517885
"Idd_direct K0E22155195556
"ado_ dizect. XDE28141E
'ade_direct, JDE3610XF_411 715',
*ado_ wtt.dtrctt_t_31100000 XDE35101F 11963610FF_75511
direct KDE3620KF_4000
Saarect__KB401226
'sde_direct. KD84014KF_S500
_300",
't_notor, AE2308KVL0E0
"t_motor_A52200
A5523089220000
"t_xotor
"t_notor_
'__nocoz_ A823177V1280',,
"s_xotor, www../2221118000
L__ooto__A23177VBE
*__ootoz_ A52911871050'
't_zotor 4228141E12000
't_notor_ A528133V2000
* t_motor__kee A528118/900', A52.09.05529188/00000
"t_motor_ A520208V1000
't_potor_ "__D00or_A122222111000 A528200V1250',
"__ooooz_ AE2920XVBBO',
"t_xotor_ AT2008V7452000
"E_Dotor_AT2300E AT2308XV2600'
"t_motor, A723108V2200',
"t_xetor, A122122V1150"
't_notoz_Atae A123122V1100
*t_motor_ AT231XV1220.
't_motor_ AT2317XV16000
't_notor, AT23172V880",
AT2321871250',
AT232187750
't_potor_ "__L0Co__AT2S1RRVLOEE AT2814XVVOE0"
"__notoz_ AT291481220'
't_xotor AT28L4EV300
[__nocos_ A728201V1250',
"t_sotor_AAe AT2820008888
't_notor AT2826XV900",
"__ooooz_ ATS520/VEE000
't_xotor_ AT35208772''
"__potor_ "__.ooor_ATTE22O
"_notor AT3C30KV680'
"t_xotor__ ATC120XVE00'
"t_notor 2ORVE66
"__oooor_ AT4125XVE40'.
"t_sotor_ AT413087450
"__potor, 1222122V7BB
"t_notor_ 12221289920'
't_xotor, NU211CK7470'
"__notor X331 10KV700
*t_motoz_ #B3119K7780'
't_xotor, NN:3008YY380
"t_potor XSKVOS
|33508897700
J0N3 10KV620
tt_notoz_
"t_xotor, NN3616EV4400
's_potor, XNB
"t_sctor, "__sotor_MM40100K 087477
"s_notor W04000KV580
"L_notoz_ H2/4022F/340*
___noco__P14012//34000
"c_sotor_ NNN4012777000
"t_potor_ 1344022VV68
*t_potor_ /244014//330'
'__potor XXE20883360
"t_motorr 22101/220_92222223444400
"c_sotor
MoooLar_TT200110000 100KK'''
't_nocoz_ (t00::co_77222112800077
barterytypo
Buttry type.
"Turnt "TurnigyGrappennn wrwpnngggrrpppeeeeeeeoe
*Fursiey@ragbenn1200uu
"Turn wtt.gggrrpceeeeeeeeeeeeaeeeeeeee
"Turni "Turni httpgrgrppppc1111000222222.2
"Turnd 2armmgggossppeme1000////000001
"Turnigy TanniggGrephonntt50aaatttte
"Turn
"Turni Durniggrdasppmmm/toooaaa---0
"Trni "TursiiyGaapeeeeeoooad.....
"Turn Seaead/@Orappeeeeecaaaatttt
"Turni "Taraiggassppmeeeeoo/aaaee
vingType
Wing type
"MACA_0012', 'HACA_0015"
"EACA_0018", SMACA_D021', 'NACA_0025',
2212",
SMACA_230'',
"HACA_ 2309'.
2312',
'MACA_2315',
"BA6A__206",
'MACA_2409"
"MACA_
2415, 'MACA_2418',
2421",
"MACA_280'',
'HACA, 2509".
2612",
"MACA, 2515",
2618",
SMACA, 2821'.
'AAGA, 2012".
"EAGA_
"MACA_1212
"BACA_4306",
SMACA_A309'
'NACAAAS12'
"815",
SMACA_A318',
'EACA, 0221",
"MACA_4405"
'HACA 4409'
'EACA 0012",
4415',
"010"_
"MACA_M111,
'NACA, 45061
0509*,
"NACA_
"UACA_A615*,
SMACA_4518",
'NACA 4521"
96121_
SMACA_4712
"MACA_6365
'HACA 6309'
"MACA6311''
'MACA_6321', 6318",
64D6',
'EAGA,6009', "E06,
'NACA_0112',
"MACA, (ACA.
'MACA, 6421',
"NACA, 6509",
6612",
'MACA_611'',
'NACA_ GE18',
"TACA_66217
"MCCA6661'',
6712"
servolype
Servo for control of Tlpss and ailerons.
fitstee D485IM
Wistec_DBsMM', "Hi
"Bitec_HS 22580',
'Eitec HS, 2288''
'Eitor HB, 40", "Hizec_ ES__1C 45HB", 'Bitec, HE, 5005N0"
"Bitec_llS JBB 5065N5',
"Bstec, H8, 5070N01 ; HB_______6608111
"Hiter, HR. 58855945
"Hittec H8. (85555,, "Hitec. 18_55999
"Hiter _Bs _88_55*
'Bitec_IIB JIS, 36666'''
'Bitec, 620M0', 'Hites C4SMMC 'Hites GEBB" 'Hisec._is, /
"Ustec_lll, 60N6' "Hites_iiS, TOMS", 'iittec_is, 723699''
'Bitac_H8__81 ", tee_ _S_62 'Hitec_RR8__S0 "Ritwe_ HS. HSM!
wingRot
The wing's 's angle.
Flost: [0, 360]
chord
The wing's 's chard.
Hozz: 1100, 500]
span
The wing's 's span.
Hozz: [200, 2000]
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2 confinued from pres page
Kry
DESCRIPTION
VALUE OPTIONS
araLongth
consext specific am length generally associated with bypes
Hoaz: 120, 300)
'Prophmm
and 'Vingles?.
ammlleagth
more specific arm length ussoci- ated with urms that include bend.
Flot: [50, 500)
amallangth
more specific arm length associ-
Floar [50, 200]
med with arma that inclade hend.
angle
A context specific angle that corre- angles the veticle struction.
Hoaz: [-135, 300]
specidic can correspond to stractural compo- nents such as the wing ollset and the flange officet
xl_offect
Offset direction of Buttery
Floz: (0, 120)
Offset e-drection Buttery contral plate.
Luba_offawt
wing take
(10, 90]
G
Aircraft design summary
In the additional supplementary material, include the complete representation of an individual design when accessed from AircraftVerse, where we print the:
tesign_tree.json
brsign_seq-json
cadfile.ste
pointCloud. mpy
trims.npy
As part of the notebook, show how to access the design corpus and provide example of printing the specification of a battery. Finally, we also include an enlarged plot of the trim states for readability, as Figure was illistration of full design that by necessity limited the allocated space for it in the full paper.
20


Reviews:
[{"review_id": "2SJ5e6j6DU", "reviewer": "Reviewer_HgCT", "summary": "", "questions": "", "limitations": "- The authors consider only a specific domain devoted to specific type of designs of  Electric Vertical Take-Off and Landing aircrafts\n- Limited bechmark. For example, for point clouds there exists dozens of point-net like architectures. However, the authors considered only one example of such type of architectures\n- It is not clear what is the exact purpose of the benchmark. Which class of algorithms do the authors want to benchmark?\n- Are there any examples of solving design optimization problem using the benchmark? \nDo the author plan to consider such type of applications to compare different methods?\n- Are there any real designs of aircrafts in the database? If we start the design optimization process from such designs there are more chances to get something feasible as the resulting optimized design", "rating": 7, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "- Interesting, non-standard dataset: multi-modal data (3D data, symbolic representations data, etc.)\n- Specially selected physically feasible designs\n- Pre-calculated physical characteristics of the design", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "CE1C6Avs8k", "reviewer": "Reviewer_prSM", "summary": "", "questions": "", "limitations": "The authors did not explicitly discuss the limitations of this work in their paper. See the \"Opportunities For Improvement\" for my suggestions.", "rating": 7, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "- The creation of the AircraftVerse dataset is a significant contribution. The dataset contains 27,714 diverse air vehicle designs, making it the largest corpus of engineering designs with such complexity. This provides a valuable resource for researchers and engineers working in the field of aerial vehicle design.\n\n- The authors have made efforts to capture the multimodal nature of aircraft design. Each design in the dataset includes a symbolic design tree, a Standard for the Exchange of Product (STEP) model data, a 3D CAD design, a 3D point cloud representing the design's shape, and evaluation results from high-fidelity state-of-the-art physics models. This level of detail allows for a more nuanced analysis and understanding of the designs.\n\n- The paper presents baseline surrogate models for predicting design performance. This not only demonstrates the potential usage of the dataset, but also provides a starting point for researchers who want to utilize this dataset for their own studies.\n\n- The dataset and the analysis presented in the paper span across several disciplines, including engineering, computer science, and machine learning. This underscores the potential of such work to drive interdisciplinary research and foster innovation in these fields.", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "an7BRRCDhd", "reviewer": "Reviewer_vKs1", "summary": "", "questions": "", "limitations": "(1) The dataset is not available now;\n(2) A tutorial is highly expected. \n(3) Who are the target users? Drone desiners or machine learning researcher?", "rating": 6, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "(1) This work is quite new for drone design, which can be called an application of 'AI for Engineering'.\n(2) The data have been carefully collected and grouped.", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "Rhi98TE0EJ", "reviewer": "Reviewer_v2Uf", "summary": "", "questions": "", "limitations": "There isn't a section to cover the limitation of the paper, at least it is not make clear to the reviewer", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "-This paper addresses the scarcity of large CAD datasets in a specific domain, particularly those incorporating aircraft metadata from FDM examinations.\n-The dataset includes a design tree, facilitating aircraft assembly and benefiting evaluation models utilizing compositional assembly (e.g., LEGO assembly paper \"Break and make: Interactive structural understanding using lego bricks\").\n-Furthermore, the paper extensively evaluates the dataset using various baseline methods.", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "tPq3vWwfpb", "reviewer": "Reviewer_TaAN", "summary": "", "questions": "", "limitations": "- Besides comments about the fidelity of the flight dynamics model, other dataset limitations were not listed in the paper. To expand this information in the main paper, I could envision that the authors might discuss limitations of their generative design process (e.g., why were so many unusable designs generated that required post-hoc filtering?) or the limitations of characterizing overall aircraft performance through shape files and component performance data. For example, on the scale of simple simulations to real-world full-scale wind tunnels, what is missing from this paper's flight dynamics model to characterize the real-world performance of these designs?\n", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "- Standardizing and indexing tens of thousands of 3D engineering models of aircraft is no small feat, and the authors' efforts in compiling not just geometric descriptors (e.g., shape) but also functional descriptors (e.g., hover time, flight distance) is very helpful for future research efforts.\n\n- AircraftVerse provides data from a non-traditional machine learning domain (aircraft design and functionality).", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "JZYcfALFed", "reviewer": "Reviewer_29GD", "summary": "", "questions": "", "limitations": "I think authors provided a fair description of their dataset and potential imitations, given its experimental nature.\n", "rating": 9, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "### Main strengths\n#### A novel multi-modal dataset pioneering the field\nThe authors addressed an existing gap in the domain of aircraft designs, which seems well grounded and seriously developed. This is clearly the principal contribution of this work.\n#### Aiming to address a gap in multi-modal holistic engineering design\nThere were no available corpus of such diverse aircraft designs accompanied by their design details and also the results from dynamic flight simulations and/or additional engineering evaluations.\n#### Performance Metrics.\nThe dataset includes evaluation results from high fidelity physics models (both custom flight dynamic simulators and commercial, i.e. Creo), enabling researchers to explore and analyze performance metrics such as maximum flight distance and hover-time for each design.\n", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}]

Author rebuttal:

Meta-review: This paper presents a large dataset of aircraft assets (over 27K) and contains multi-modal representations for each model (e.g., STL, Point Cloud, STEP, etc.). The models have complicated structures and compositions of parts for making aircraft. The authors have done preliminary experiments using the proposed dataset on the task of predicting the performance characteristics of an aircraft given symbolic design trees as inputs. While there are some concerns regarding the discussion of limitations (which has been well addressed by the authors during rebuttal), broader applications of the dataset, and the fact that the models are probabilistically generated, most reviewers and AC agree on the contributions of this dataset since it fills in the gap of an important and interesting yet underexplored data domain, and its large scale and multi-modal richness, as well as demonstrating some examples for using the dataset. Therefore, an acceptance decision is recommended.
Decision: Accept (Poster)