ccm/publications huggingface dataset:
HUVER: The HyForm Uncrewed Vehicle Engineering Repository
Authors: Abhiram Karri and Gary Stump and Christopher McComb and Binyang Song
Published in: Journal of Mechanical Design, 2025

This paper introduces the HyForm Uncrewed Vehicle Engineering Repository (HUVER), a comprehensive multi-modal dataset of uncrewed aerial vehicle (UAV) designs, complete with performance evaluations, derived from the HyForm UAV design testbed. The dataset includes 6,051 unique UAV configurations, each represented using strings adhering to a designed grammar, images, 3D mesh models, and textual descriptions, alongside performance metrics obtained from physics-based simulations. Designed to support data-driven and AI-driven design processes, one area in which this dataset can facilitate research is the surrogate modeling and generative design of UAVs, providing a resource for developing predictive models and supporting human-AI collaboration in UAV design. The dataset adheres to FAIR principles, ensuring it is retrievable, accessible, interoperable, and reusable, and is made available as …
Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment
Authors: Akash Agrawal and Christopher McComb
Published in: arXiv preprint arXiv:2411.10841, 2024

Multi-fidelity Reinforcement Learning (RL) frameworks significantly enhance the efficiency of engineering design by leveraging analysis models with varying levels of accuracy and computational costs. The prevailing methodologies, characterized by transfer learning, human-inspired strategies, control variate techniques, and adaptive sampling, predominantly depend on a structured hierarchy of models. However, this reliance on a model hierarchy overlooks the heterogeneous error distributions of models across the design space, extending beyond mere fidelity levels. This work proposes ALPHA (Adaptively Learned Policy with Heterogeneous Analyses), a novel multi-fidelity RL framework to efficiently learn a high-fidelity policy by adaptively leveraging an arbitrary set of non-hierarchical, heterogeneous, low-fidelity models alongside a high-fidelity model. Specifically, low-fidelity policies and their experience data are dynamically used for efficient targeted learning, guided by their alignment with the high-fidelity policy. The effectiveness of ALPHA is demonstrated in analytical test optimization and octocopter design problems, utilizing two low-fidelity models alongside a high-fidelity one. The results highlight ALPHA's adaptive capability to dynamically utilize models across time and design space, eliminating the need for scheduling models as required in a hierarchical framework. Furthermore, the adaptive agents find more direct paths to high-performance solutions, showing superior convergence behavior compared to hierarchical agents.
MSEval: A Dataset for Material Selection in Conceptual Design to Evaluate Algorithmic Models
Authors: Yash Patawari Jain and Daniele Grandi and Allin Groom and Brandon Cramer and Christopher McComb
Published in: Journal of Mechanical Design, 2024

Material selection plays a pivotal role in many industries, from manufacturing to construction. Material selection is usually carried out after several cycles of conceptual design, during which designers iteratively refine the design solution and the intended manufacturing approach. In design research, material selection is frequently modeled as a structured decision-making process, where optimization techniques, whether single-objective or multiobjective, are employed to identify solutions that best meet the design requirements. However, traditional approaches can be limited by their reliance on existing knowledge and data, which may not adequately capture the full range of considerations involved in material selection. In this article, we introduce MSEval, a novel dataset comprised expert material evaluations across a variety of design briefs and criteria. This dataset is designed to serve as a benchmark to facilitate …
Design by Data: Cultivating Datasets for Engineering Design
Authors: Faez Ahmed and Cyril Picard and Wei Chen and Christopher Mccomb and Pingfeng Wang and Ikjin Lee and Tino Stankovic and Douglas Allaire and Stefan Menzel
Published in: Journal of Mechanical Design, 1-8, 2025

This special issue of the Journal of Mechanical Design showcases a collection of research articles focused on the crucial role of datasets in advancing the field of engineering design. Through these articles, we aim to underline the transformative impact of data-driven methods, which have reshaped numerous domains, and show their potential to reshape engineering design. However, the adoption of these methods in engineering faces hurdles such as the scarcity of high-quality diverse datasets. This editorial not only highlights these challenges but also offers recommendations for developing impactful design datasets and outlines a vision for their systematic use. To ensure transparency and facilitate effective utilization of these datasets, we advocate for the adoption of a standardized datasheet for design datasets in engineering design research.
Multi-lattice Topology Optimization via Generative Lattice Modeling
Authors: Jiangce Chen and Martha Baldwin and Sneha Narra and Christopher McComb
Published in: Journal of Mechanical Design, 2025

Additive manufacturing enables the fabrication of multi-lattice structures, an advanced design approach featuring heterogeneous lattices at the mesoscale which are arranged to achieve a diverse and purposeful distribution of material properties at the macroscale. Compared to uniform lattice structures, multi-lattice structures permit greater design freedom and a larger design space, which makes it possible to achieve superior structure performance. However, the expanded design space introduces a substantial increase in the complexity that must be managed in order to achieve a multi-lattice structure solution. However, there is a lack of design automation approaches that can tractably create multilattice structures. This article introduces an innovative multi-scale topology optimization (TO) framework, called multi-lattice topology optimization with variational autoencoder (MulaTOVA), that is capable of concurrently …
Evaluating Community Well-being and Resilience via Agent-Based Models
Authors: Aslan Noorghasemi and Ming Xiao and Christopher McComb
Published in: AGU24, 2024

Infrastructure provides an intricate system through which humans interact with each other and their environment, which is essential for community well-being. These systems, however, degrade over time and become less usable, either due to wear or abrupt damage. Therefore, it becomes necessary to allocate resources to maintain the infrastructure and ensure the community's well-being. However, the relationship between infrastructure expenditures and community well-being is unclear and challenging to measure. This is especially true in locations like Utqiagvik, Alaska, where permafrost thaw due to global warming threatens a substantial amount of infrastructure. Current computational models are inadequate for predicting the cascading effects of system failures, particularly under extreme environmental conditions. This paper introduces agent-based modeling (ABM) as a more adaptive and insightful method to study the impact of infrastructure on community well-being. This approach provides a detailed analysis of component contributions to system robustness, identifying key vulnerabilities for prioritized maintenance and resource allocation.
Enforcing the Principle of Locality for Physical Simulations with Neural Operators
Authors: Jiangce Chen and Wenzhuo Xu and Zeda Xu and Noelia Grande Gutierrez and Sneha Prabha Narra and Christopher McComb
Published in: Journal of Computational Physics, 2025

Time-dependent partial differential equations (PDEs) for classic physical systems are established based on the conservation of mass, momentum, and energy, which are ubiquitous in scientific and engineering applications. These PDEs are strictly local-dependent according to the principle of locality in physics, which means that the evolution at a point is only influenced by the neighborhood around it whose size is determined by the length of timestep multiplied with the speed of characteristic information traveling in the system. However, deep learning architecture cannot strictly enforce the local-dependency as it inevitably increases the scope of information to make local predictions as the number of layers increases. Under limited training data, the extra irrelevant information results in sluggish convergence and compromised generalizability. This paper aims to solve this problem by proposing a data decomposition …
Guiding Generalized Team Problem Solving through a Collective Intelligence-Based Artificial Intelligence Facilitator
Authors: Elizabeth S McGee and Jonathan Cagan and Christopher McComb
Published in: Journal of Mechanical Design, 2025

The use of Artificial Intelligence (AI) to guide team dynamics has the potential to transform collaborative problem-solving processes. Existing approaches to doing so are trained on prior problem-specific data, limiting them to problems that have already been solved. This research aims to extend AI-based approaches to novel situations by eliminating the need for prior data. This is accomplished by focusing on team communication and collective intelligence (CI) rather than problem-specific strategies. CI is a team's general ability to work well across various tasks and is more predictive of team performance than individual intelligence. This work introduces an AI Facilitator that monitors CI attributes–collective attention, equal participation, and consistent communication–in real-time and intervenes as necessary to guide teams towards better collaboration and overall performance. Two human subjects studies are …
MEDA: A Multi-agentic System for Parametric CAD Model Creation
Authors: Nirmal Panta and Saugat Kafley and Rujal Acharya and Sashank Parajuli and Dikshya Parajuli and Prince Panta and Saroj Belbase and Sudikshya Pant and Amit Regmi and Akio Tanaka and Christopher McComb
Published in: International Design Engineering Technical Conferences and Computers and …, 2025
KABOOM: An Agent-Based Model for Simulating Cognitive Style in Team Problem Solving
Authors: Samuel Lapp and Kathryn Jablokow and Christopher McComb
Published in: Design Science, 2019

The performance of a design team is influenced by each team member’s unique cognitive style – i.e., their preferred manner of managing structure as they solve problems, make decisions, and seek to bring about change. Cognitive style plays an important role in how teams of engineers design and collaborate, but the interactions of cognitive style with team organization and processes have not been well studied. The limitations of small-scale behavioral experiments have led researchers to develop computational models for simulating teamwork; however, none have modeled the effects of individuals’ cognitive styles. This paper presents the Kirton Adaption–Innovation Inventory agent-based organizational optimization model (KABOOM), the first agent-based model of teamwork to incorporate cognitive style. In KABOOM, heterogeneous agents imitate the diverse problem-solving styles described by the Kirton …
Lifting the Veil: Drawing Insights about Design Teams from a Cognitively-inspired Computational Model
Authors: Christopher McComb and Jonathan Cagan and Kenneth Kotovsky
Published in: Design Studies 40, 119-142, 2015

Novel design methodologies are often evaluated through studies involving human designers, but such studies can incur a high personnel cost. It can also be difficult to isolate the effects of specific team or individual characteristics. This work introduces the Cognitively-Inspired Simulated Annealing Teams (CISAT) modeling framework, a platform for efficiently simulating and analyzing human design teams. The framework models a number of empirically demonstrated cognitive phenomena …
Learning To Design From Humans: Imitating Human Designers Through Deep Learning
Authors: Ayush Raina and Christopher McComb and Jonathan Cagan
Published in: Journal of Mechanical Design, 2019

Humans as designers have quite versatile problem-solving strategies. Computer agents on the other hand can access large-scale computational resources to solve certain design problems. Hence, if agents can learn from human behavior, a synergetic human-agent problem-solving team can be created. This paper presents an approach to extract human design strategies and implicit rules, purely from historical human data, and use that for design generation. A two-step framework that learns to imitate human design strategies from observation is proposed and implemented. This framework makes use of deep learning constructs to learn to generate designs without any explicit information about objective and performance metrics. The framework is designed to interact with the problem through a visual interface as humans did when solving the problem. It is trained to imitate a set of human designers by …
A cautionary tale about the impact of AI on human design teams
Authors: Guanglu Zhang and Ayush Raina and Jonathan Cagan and Christopher McComb
Published in: Design Studies 72, 100990, 2021

Recent advances in artificial intelligence (AI) offer opportunities for integrating AI into human design teams. Although various AIs have been developed to aid engineering design, the impact of AI usage on human design teams has received scant research attention. This research assesses the impact of a deep learning AI on distributed human design teams through a human subject study that includes an abrupt problem change. The results demonstrate that, for this study, the AI boosts the initial performance of low-performing teams before …
Design Repository Effectiveness for 3D Convolutional Neural Networks: Application to Additive Manufacturing
Authors: Glen Williams and Nicholas A Meisel and Timothy W Simpson and Christopher McComb
Published in: Journal of Mechanical Design, 2019

Machine learning can be used to automate common or time-consuming engineering tasks for which sufficient data already exist. For instance, design repositories can be used to train deep learning algorithms to assess component manufacturability; however, methods to determine the suitability of a design repository for use with machine learning do not exist. We provide an initial investigation toward identifying such a method using “artificial” design repositories to experimentally test the extent to which altering properties of the dataset impacts the assessment precision and generalizability of neural networks trained on the data. For this experiment, we use a 3D convolutional neural network to estimate quantitative manufacturing metrics directly from voxel-based component geometries. Additive manufacturing (AM) is used as a case study because of the recent growth of AM-focused design repositories such as …
Optimizing Design Teams Based on Problem Properties: Computational Team Simulations and an Applied Empirical Test
Authors: Christopher McComb and Jonathan Cagan and Kenneth Kotovsky
Published in: Journal of Mechanical Design, 2017

The performance of a team with the right characteristics can exceed the mere sum of the constituent members' individual efforts. However, a team having the wrong characteristics may perform more poorly than the sum of its individuals. Therefore, it is vital that teams are assembled and managed properly in order to maximize performance. This work examines how the properties of configuration design problems can be leveraged to select the best values for team characteristics (specifically team size and interaction frequency). A computational model of design teams which has been shown to effectively emulate human team behavior is employed to pinpoint optimized team characteristics for solving a variety of configuration design problems. These configuration design problems are characterized with respect to the local and global structure of the design space, the alignment between objectives, and the resources …
Rolling with the Punches: An Examination of Team Performance in a Design Task Subject to Drastic Changes
Authors: Christopher McComb and Jonathan Cagan and Kenneth Kotovsky
Published in: Design Studies 36, 99-121, 2015

Designers must often create solutions to problems that exhibit dynamic characteristics. For instance, a client might modify specifications after design has commenced, or a competitor may introduce a new technology or feature. This paper presents a cognitive study that was conducted to explore the manner in which design teams respond to such situations. In the study, teams of undergraduate engineering students sought to solve a design task that was subject to two large, unexpected changes in problem formulation that were introduced during solving. High-and low-performing teams demonstrated very different …
Capturing Human Sequence-Learning Abilities in Configuration Design Tasks through Markov Chains
Authors: Christopher McComb and Jonathan Cagan and Kenneth Kotovsky
Published in: Journal of Mechanical Design 139 (9), 2017

Designers often search for new solutions by iteratively adapting a current design. By engaging in this search, designers not only improve solution quality but also begin to learn what operational patterns might improve the solution in future iterations. Previous work in psychology has demonstrated that humans can fluently and adeptly learn short operational sequences that aid problem-solving. This paper explores how designers learn and employ sequences within the realm of engineering design. Specifically, this work analyzes behavioral patterns in two human studies in which participants solved configuration design problems. Behavioral data from the two studies are first analyzed using Markov chains to determine how much representation complexity is necessary to quantify the sequential patterns that designers employ during solving. It is discovered that first-order Markov chains are capable of accurately …
Mining Process Heuristics from Designer Action  Data via Hidden Markov Models
Authors: Christopher McComb and Jonathan Cagan and Kenneth Kotovsky
Published in: Journal of Mechanical Design, 2017

Configuration design problems, characterized by the assembly of components into a final desired solution, are common in engineering design. Various theoretical approaches have been offered for solving configuration type problems, but few studies have examined the approach that humans naturally use to solve such problems. This work applies data-mining techniques to quantitatively study the processes that designers use to solve configuration design problems. The guiding goal is to extract beneficial design process heuristics that are generalizable to the entire class of problems. The extraction of these human problem-solving heuristics is automated through the application of hidden Markov models to the data from two behavioral studies. Results show that designers proceed through four procedural states in solving configuration design problems, roughly transitioning from topology design to shape and …
Are you feeling me? An exploration of empathy development in engineering design education
Authors: Mohammad Alsager Alzayed and Christopher McComb and Jessica Menold and Jacquelyn Huff and Scarlett R Miller
Published in: Journal of Mechanical Design, 2021

Having empathy in the design process can help engineers relate to the end-user by identifying what and why certain experiences are meaningful. While there have been efforts to identify the factors that impact empathic tendencies in engineering, there has been limited evidence on how a students’ trait empathy or empathic self-efficacy develops over a design project or what factors impact this development. The current study was developed to explore the development of students’ trait empathy and empathic self-efficacy development and identify the underlying impact of the design project’s context and course instructor through a study with 103 engineering students. Students’ trait empathy and empathic self-efficacy were measured across each of the four design stages (problem formulation, concept generation, concept selection, and final conceptual design) during an 8-week project. The results highlight that …
Toward Hybrid Teams: A Platform To Understand Human-Computer Collaboration During the Design of Complex Engineered Systems
Authors: Binyang Song and Nicolas F. Soria Zurita and Guanglu Zhang and Gary Stump and Corey Balon and Simon W. Miller and Michael Yukish and Jonathan Cagan and Christopher McComb
Published in: International Design Conference - DESIGN 2020, 2020

Human-computer hybrid teams can meet challenges in designing complex engineered systems. However, the understanding of interaction in the hybrid teams is lacking. We review the literature and identify four key attributes to construct design research platforms that support multi-phase design, hybrid teams, multiple design scenarios, and data logging. Then, we introduce a platform for unmanned aerial vehicle (UAV) design embodying these attributes. With the platform, experiments can be conducted to study how designers and intelligent computational agents interact, support, and impact each other.
Decoding the Agility of Artificial Intelligence-Assisted Human Design Teams
Authors: Binyang Song and Joshua T. Gyory and Guanglu Zhang and Nicolas F. Soria Zurita and Gary Stump and Jay Martin and Simon W Miller and Corey Balon and Michael Yukish and Christopher McComb and Jonathan Cagan
Published in: Design Studies, 2022

Although necessary for complex problem solving, such as engineering design, team agility is often difficult to achieve in practice. The evolution of Artificial Intelligence (AI) affords unique opportunities for supporting team problem solving. While integrating assistive AI agents into human teams has at times improved team performance, it is still unclear if, how, and why AI affects team agility. A large-scale human experiment answers these questions, revealing that, with appropriately interfaced AIs, AI-assisted human teams enjoy …
Transferring Design Strategies from Human to Computer and Across Design Problems
Authors: Ayush Raina and Jonathan Cagan and Christopher McComb
Published in: Journal of Mechanical Design, 2019

Solving any design problem involves planning and strategizing, where intermediate processes are identified and then sequenced. This is an abstract skill that designers learn over time and then use across similar problems. However, this transfer of strategies in design has not been effectively modeled or leveraged within computational agents. This note presents an approach to represent design strategies using a probabilistic model. The model provides a mechanism to generate new designs based on certain design strategies while solving configuration design task in a sequential manner. This work also demonstrates that this probabilistic representation can be used to transfer strategies from human designers to computational design agents in a way that is general and useful. This transfer-driven approach opens up the possibility of identifying high-performing behavior in human designers and using it to …