Open-CK: A Large Multi-Physics Fields Coupling benchmarks in Combustion Kinetics
Authors: Zaige Fei
Fan Xu
Junyuan Mao
Yuxuan Liang
Qingsong Wen
Kun Wang
Hao Wu
Yang Wang
Venue: ICLR 2025 Poster (2025)
Keywords: Fire Dynamics
Spatio-temporal Data Mining
Fluid Modeling
Primary area: learning on time series and dynamical systems
TL;DR: This work we propose openck benchmark includes various physical fields such as temperature and pressure, and covers multiple environmental combinations for exploring multi-physics field coupling phenomena.

Abstract: In this paper, we use the Fire Dynamics Simulator (FDS) combined with the {\fontfamily{lmtt}\selectfont \textit{supercomputer}} support to create a \textbf{C}ombustion \textbf{K}inetics (CK) dataset for machine learning and scientific research. This dataset captures the development of fires in industrial parks with high-precision Computational Fluid Dynamics (CFD) simulations. It includes various physical fields such as temperature and pressure, and covers multiple environmental combinations for exploring \underline{multi-physics} field coupling phenomena. Additionally, we evaluate several advanced machine learning architectures across our {\fontfamily{lmtt}\selectfont {Open-CK}} benchmark using a substantial computational setup of 64 NVIDIA A100 GPUs: \ding{182} vision backbone; \ding{183} spatio-temporal predictive models; \ding{184} operator learning frameworks. These architectures uniquely excel at handling complex physical field data. We also introduce three benchmarks to demonstrate their potential in enhancing the exploration of downstream tasks: (a) capturing continuous changes in combustion kinetics; (b) a neural partial differential equation solver for learning temperature fields and turbulence; (c) reconstruction of sparse physical observations. The Open-CK dataset and benchmarks aim to advance research in combustion kinetics driven by machine learning, providing a reliable baseline for developing and comparing cutting-edge technologies and models. We hope to further promote the application of deep learning in earth sciences. Our project is available at \url{https://github.com/whscience/Open-CK}.

Published as a conference paper at ICLR 2025
OPEN-CKO: A LARGE MULTI-PHYSICS FIELDS COU- PLING BENCHMARKS IN COMBUSTION KINETICS
Zaige Fei Fan Xu":!, Junyyan Mao', Yuxuan Liang?, Qingsong Wen?,
Kun Wang 1.1.4 Hao Wa Yang Wang
1 University of Science and Technology of China
The Hong Kong University of Science and Technology (Guangzhou)
3 Squirrel Ai Learning
Nanyang Technological University
fzaige, markxu, mao junyuan, wuhao2022, anqyan)@mail wsw.cstccc... edu.cn, [yuxiiang] @ooutoock com, fgingsongedu, 5j675627966 00mmi com
ABSTRACT
In this paper, we use the Fire Dynamics Simulator (FDS) combined with the supercomputer support to create a Combustion Kinetics (CK) dataset for ma- chine learning and scientific research. This dataset captures the development fires in industrial parks with high-precision Computational Fluid Dynamics (CFD) simulations. It includes various physical fields such as temperature and pressure, and covers multiple environmental combinations for exploring multi-physics field coupling phenomena. Additionally, we evaluate several advanced machine learn- ing architectures across our Open-CK benchmark using substantial computa- tional setup of 64 NVIDIA A100 GPUs: O vision backbone; 2 spatio-temporal predictive models; @ operator learning frameworks. These architectures uniquely excel at handling complex physical field data. We also introduce three bench- marks to demonstrate their potential in enhancing the exploration of downstream tasks: (a) capturing continuous changes in combustion kinetics; (b) a neural par- tial differential equation solver for learning temperature fields and turbulence; (c) reconstruction of sparse physical observations. The Open-CK dataset and bench- marks aim to advance research in combustion kinetics driven by machine learning, providing reliable baseline for developing and comparing cutting-edge technolo- gies and models. We hope to further promote the application of deep learning earth sciences.
INTRODUCTION
Modern deep learning (DL) approaches have demonstrated promising outcomes various dynami- cal systems in natural and social science fields like weather forecasting Schultz al. (2021); Pathak et al. (2022); Bi et al. (2022), rapid fire progression Tam et al. (2022), and intelligent transportation Kaffash et al. (2021); Jin et al. (2023). Such astonishing achievements primarily stem from two crucial factors. First, with the development of computer science, a vast amount of data from Earth systems continuously being acquired Chen al. (2022); Liu et (2023). These ever-growing, massive datasets, with diverse sources, provide the impetus for data-hungry deep models, making learning from data possible. Second, continual breakthroughs in DL algorithms and models enable us to effectively adapt to diverse specific scenarios, resulting in state-of-the-art performances Wu et al. (2024a); Wang et al. (2024); Wu et al. (2023b; 2024c); Hao et al. (2024).
Fluids, essential data type within the realm of earth sciences Ferziger et al. (2019); Temam (2001), are characterized by molecular structure that lacks resistance to external shear forces. This inherent property allows fluids to deform readily, even under minimal forces, often resulting in dynamics that are highly complex both spatially and temporally Ma et al. (2024); Yu et al. (2018); Wang al. (2021; 2022a). To date, ever-increasing focus data-driven deep methods, while paving
"Equal contribution Corresponding author

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Pressure
Temperature Field
Velocity Field
Velocity Field- - Colored Streamplot
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The figure shows the changes in different physical fields during an industrial park fire simulation, including the pressure field, temperature field, velocity field, and a colored streamplot of the velocity field. The simulation conditions are a single fire source and wind direction (10 mW combustion power, m/s wind speed, 180 seconds simulation time)
Figure 1:
a potential path for large-scale fluid dynamics studies, unfortunately poses a daunting obstacle in collecting high-quality fluid data Chen et al. (2022); Veillette et al. (2020).
A quick review of existing literature reveals that numerous benchmarks involving sensor collection and numerical simulations related fluid dynamics have been proposed. In the field of meteorology, datasets such as RainNet Ayzel et al. (2020), ERA5 Muñoz-Sabatee et al. (2021), and WeatherBench Rasp et al. (2020) have collected high-quality meteorological data. These resources offer detailed measurements of rainfall, hurricanes, temperature, climatic variables related to land and oceans, which provide invaluable resources for subsequent meteorological model development like Pangu- Weather Bi et al. (2022). In the ocean domain, HYCOM Chassignet et al. (2007), ECCO Forget et al. (2015), and CMEMS provide detailed measurements ocean temperature, salinity, currents, and sea surface height, offering valuable data for ocean model development. Going beyond this process, Many fluid benchmarks across terrestrial environments Justice et al. (2002;?); Rodell et al. (2004) and spring systems Otness al. (2021) provide foundational research platforms for this field Wu et al. (2023b).
Though promising, combustion safety, which is crucial for human life, lacks high-quality standard- ized datasets and evaluation protocols. Existing combustion dynamics datasets like WildfireDB Ger- ard al. (2024), FireSpread Gerard al. (2024), and Prometheus Wu et al. (2024b) have limitations. WildfireDB and FireSpread mainly rely on actual image data, which limits diversity and coverage and lacks physical significance. These datasets also face data imbalance and noise issues, affecting model training. While Prometheus provides high-quality data, its low spatial and temporal resolution fails to capture complex fire dynamics. Additionally, these datasets lack multi-physics coupling data, limiting model generalization across scenarios. Therefore, we develop the Open-CK Benchmark. Open-CK ()) is the first open-source benchmark dedicated to the study of combustion fluid dynam- ics, created through over 360 hours of numerical simulations supported by super supercomputers This dataset captures the ST variations of multiple physical fields (as shown in Figure 1), encom- passing 300 scenarios with varying parameters such as heat release rates (HRR) of Standards & Technology (2023), temperature, source location, etc, contributing to the development of a standard evaluation protocol in fire safety research.
Open-CK involves several PDEs, including the Navier-Stokes Li et al.; Takamoto et al. (2022), mass conservation Jain & Kennedy (2014), energy conservation McGrattan et al. (2010), the Heat Conduction Tieszen (2001), and the Transport Equation for Smoke and Chemical Species Drys- dale (2011). The Fire Dynamics Simulator (FDS) Hietaniemi al. (2004), computational fluid dynamics (CFD) Hirsch (2007) software, leverages these PDEs to simulate the complex dynamics of combustion McGrattan et al. (2006), including flame propagation, smoke flow, heat conduction, and radiation. By solving these equations, FDS provides detailed simulations and analyses of fire scenarios. Using FDS, we generated 4480GB of raw data in CSV format, organized dimensional (time-sensor) structure. To facilitate research using S we preprocessed this data into a four-dimensional (D₃ ~ D4) format: D1 represents sampling time, D2 the type of physical field (velocity, temperature, pressure), and Di and D2 represent the two-dimensional spatial distribution of sensors. This preprocessing compresses the raw data into a more manageable size (~2.2TT) and formats it into npy files Harris et al. (2020) suitable for DL applications.
two-
Boarder Impact. has the following features and advantages: Firstly, we use FDS simu- lations to generate various industrial park fire scenarios. Unlike machine learning benchmark
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PDEBENCH's Takamoto et al. (2022) single physical field, Open-CK covers multiple physical fields, including temperature, pressure, and velocity, with high spatial and temporal resolution. Sec- ondly, compared to large CFD-based fire set EAGLE Janny et al. (2023) and Prometheus's Wu et al. (2024b) single grid environments, I supported by sizes, ensuring data complexity and high physical consisteney. Lastly, we validate the dataset's effectiveness through multiple SOTA models. By comparing various advanced machine learning models, we demonstrated the dataset's ' potential in combustion kinetics research. Our study not only provides a scientific basis for fire prediction and management but also opens new avenues for the application of deep learning Earth sciences.
simulates grids of differ-
2 PRELIMINARIES
2.1 PROBLEM FORMULATION
This study explores the ST evolution of fire dynamics, where the input data is represented as a four-dimensional array [T', C, H. W1. T denotes the time steps, C' represents channels for different physical values (e.g., temperature R and pressure V), and and W correspond to the vertical and horizontal spatial resolutions, respectively. To predict future combustion dynamics behavior, utilize a model based on historical data, employing Maximum Likelihood Estimation (MLE) for parameter estimation. Let the model output Y, represent the state of combustion at time i, and X, contains all prior time step data, i.e., X, = [ I2,..., Our goal is to estimate Y the state at the next time step, through the predictive model f(-). The mathematical representation is as follows:
can
Y++1 = S(X,,
II 1014+1a4;
(I)
where θ is the model parameter. Using MLE, we aim to find the parameters @ that maxi- mize the likelihood P(Y--1XX; O) given X₃- By maximizing the log-likelihood function L, an estimate of the parameters O can be obtained as Ô = arg max, log [(0; Xx, Y++1) = arg max; E log P O). Scrutinizing above optimization, the model can not only ef- fectively utilize Xr predict future combustion dynamics Y but also perform predictions over multiple time steps, providing a scientific basis for fire management and control.
2.2 TECHNICAL BACKGROUND
Scientific Machine Learning Datasets. Multiple efforts aim to develop benchmark datasets for scientific machine learning. WeatherBench Rasp et al. (2020; 2023) offers global high-resolution weather forecasting data, covering meteorological variables like temperature, pressure, and wind speed. The ERAS dataset Muñoz-Sabater et al. (2021), organized by the European Centre for Medium- Medium-Range Weather Forecasts (ECMWF), has provided hourly estimates of atmospheric, land, and occanic climate variables since 1940, becoming a popular resource for weather prediction. has supported the development of neural weather models like FourCastNet Pathak et al. (2022), Pangu- Weather Bi et al. (2023), and Graphcast Lam et al. (2022). PDEBENCH Takamoto et al. (2022) suite for scientific machine learning models dealing with systems governed by PDEs, which provides di- verse initial and boundary conditions and introduces new metrics to assess scientific performance. Primarily focused on time-dependent flow problems, PDEBENCH does not include complex sce- narios like multi-phase flows or non-rectangular domains. Large benchmark EAGLE Janny et al. (2023) contains 1.1 million 2D grids simulated based on Computational Fluid Dynamics (CFD) techniques, generated by simulations of non-constant hydrodynamics induced by the interaction of a moving flow source with a nonlinear scene structure; however, all snapshots are too low-resolution for the analysis of complex phenomena. WilddireSpreadTS Gerard et al. (2024) is and modll dataset with 13,607 images from 607 U.S. wildfires from 2018 to 2021. It supports high-resolution predictions of wildfire spread using its time series structure and multi-modal inputs. However, the dataset faces challenges due to complex inputs, imbalanced labels, and noisy data.
Dynamic System Modeling (DSM). Various advanced architectures that each offer unique features for addressing complex problems in DSM realm. 1 Vision backbone architectures like U-Net and its variants Ronneberger al. (2015); Huang al. (2020); Li et al. (2018); Weng al. (2019), with their
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symmetrical encoder-decoder structure, optimize feature extraction from high-resolution images, making them ideal for super-resolution tasks in meteorological data. ResNet He et al. (2016) in- troduces residual connections to combat gradient vanishing, enhancing learning capabilities. Vision Transformer (ViT) Dosovitskiy et al. (2021) effectively captures global image dependencies through its self-attention mechanism, suited for large-scale, complex physical field data Pathak et al. (2022). @ In spatio-temporal prediction realm, ConvLSTM Shi et al. (2015) merges the strengths of convolu- tional networks and long short-term memory networks handle spatial correlations and time series data efficiently. Earthformer Gao et al. (2022b) leverages the Transformer's s encoding power for large-scale earth science data processing. Si Tan et al. (2022) efficiently predicts future states of dynamic systems with a simplified architecture. @ In operator learning field, architectures like Fourier Neural Operator (FNO) Li et al. (2020), U-shaped Neural Operators (UNO) Ashiqur Rah- man et al. (2022), and Convolutional Neural Operator (CNO) Raonic et al. (2024) innovate in Fourier space, multi-scale representations, and convolutional structures, respectively, offering precise and universal solutions for solving partial differential equations. These architectures advance dynamic system modeling and expand opportunities for scientific research and industrial applications.
3 OPEN-CK: THE NON-LINEAR CHAOTIC COMBUSTION KINETICS BENCHMARK
In this section, we formally introduce the Open benchmark, designed to comprehensively assess the accuracy, efficiency, and fidelity of existing and future vision backhones, ST predictive models, and neural operator frameworks. Initially, we provide detailed explanation of the collection and organization of Open-CK in Section 3.1. In Section 3.2, we introduce the dataset management and workflow, followed by in-depth explanation of further preprocessing steps to enhance our understanding of Open-CK. Subsequently, in Section 3.3, we analyze the data distribution of Open- CK and present the characteristies of the dataset. Finally, in Section 3.4, we outline the licensing of the resources.
3.1 DATA COLLECTION AND ORGANIZATION
To effectively train deep learning models for modeling and predicting fire scenarios in industrial parks, we collect a large set of simulated data. We generate this data using version 6.9.1 of the FDS developed by the National Institute of Standards and Technology (NIST), based on CFD simu- lations. The simulations occur in typical industrial park with multiple oil storage areas, modeling various fire development scenarios. The simulated scenario appears as part (I) in Figure 2(a). We create 300 different fire scenarios, each with specific settings for fuel type, heat release rate (HRR), fire growth factor, wind speed, wind direction, and ignition location. We run all simulations on a with each scenario taking approximately seven to ten hours. To accurately cap- ture the dynamic changes in the physical field during fires, we place a dense sensor grid at a height of five meters within the simulation area, with sensors spaced every 0.5 meters. The sensor arrange- ment appears as part (II) in Figure 2(a). These sensors collect data every second, covering multiple physical parameters such as velocity, temperature, and pressure, as detailed in Table 1. We organize the collected data into time series, with each data point containing the full physical parameters of the scene each moment, ensuring data integrity and continuity. We then format these data into struc- ture suitable for deep learning model input, providing a high-quality, dense numerical database for algorithm training and validation. This approach not only allows us to study the physical behaviors in various fire scenarios in detail but also to optimize and adjust the deep learning models based on these simulation results, improving their application
fire prediction and management.
AutoCAD is widely used computer-aided design (CAD) software for precise creation of both 2D and 3D drawings and models. We used AutoCAD software to create a 3D model of an industrial park in real-world scenario (as shown in Figure 9). The resulting DWG files² were then imported into PyroSim software" for the preliminary design work required for numerical simulation (as shown in Figure 12). PyroSim is software tool commonly used for fire and evacuation simulation in
'https: / /www. autodesk. com coompppodccs//aaaeooooooeaaeeeee http:/ddttttttttccc///eeeeeeeeee /vvevviee
PDWG (short for "drawing") is proprictary binary file format used for storing two- and three-dimensional design data and metadata. https: www.ws....eekkkkkk.kkk.k com/ colutiio intionn /dwg
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Vel (m/
Temperature Velocity
Tem (C)
Pres (Pa)
Pressure
30s
60s
90s
120s
Figure describes the overall layout simulated industrial park experiment. shows visualizations of different physical fields changing over time.
complex environments. This design work includes setting parameters such as mesh size and density, fire source location and type. ventilation conditions, HRR, and fire growth coefficient. This process generates the FDS files* corresponding to the various scenarios.
Python is widely used programming language. Due the large number and variety of sensors needed, we wrote a Python script (as shown in Figure 14) to directly edit the FDS files and create required sensor arrays. After running the FDS files numerical simulation, we obtained the data collected by all the sensors. This data is saved in CSV format, where the columns represent all sensors and the rows represent all recorded time points. For illustration, in particular scenario, we set up three types of sensors, each with 90,000 units. This scenario simulated the fire development process over 400 seconds, with a data collection frequency of Hz. Thus, the resulting CSV file has dimensions of (400, 270000). Additionally, each numerical simulation generates SMV file",
*FDS files are input files for the Fire Dynamics Simulator, CFD model of fire-driven fluid flow. These files contain data the geometry, material properties, and initial conditions needed to simulate fire scenarios. More information can be found https:: ttttts://pags...eet 'tt t pa / /ww., python. org/
gov/ Eds- smv/
An SMV file is visualization file format used by Smokeview, companion software the FDS.

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Table 1: Summary of Open-CK. NIS: Number of Ignition Sources; SIS: Single Ignition Source; TIS: Three
Ignition Sources; Tem: Temperature: Vel: Velocity; Pres: Pressure.
NIS
Physical Field
Domain
Spatial Resolution
At
Timesteps
SIS
Tem& Pres
300m x 300m
300 x 300
350
SIS
Vel
100m x 70m
220 x 140
300
SIS
Tem
20m x 20m
40 40
0.5
250
SIS
Pres
20m x 20m
20 20
350
TIS
Tem& Pre&
300m x 300m
600 x 600
TIS
Tem
110m x 70m
220 x 140
300
TIS
Vel
50m x 50m
50 50
0.5
300
TIS
Pres
20m x 20m
40 40
350
Dataset Curation and Workflow
Vision Backbone
Data preprocessing
ST models
Checkpoint
Load &
Neural Operator
Github
Open-CK.apy
PYTORCH
inference
Figure 3: Dataset Curation and Workflow. This figure shows the process from data preprocessing to model training. saving, and application. Data is preprocessed using Python, analyzed and features extracted via vision backbone, ST models, and neural operator in PyTorch. The trained model is saved checkpoint, uploaded to GitHub for storage and version control, and used for inference.
which can be visualized using PyroSim's PyroSim Results application, feature within the PyroSim software designed for viewing, analyzing, and interpreting simulation results (Figure 13).
To preprocess the data for use in deep learning models, we used Python scripts (see Figure 15) to filter and clean the data in the CSV files. The data was then reshaped to dimensions (T, Dt, R, C), where T represents the time steps, Dt represents sensor types, R represents the sensor rows, and C represents the sensor columns. Thus, we obtained the preliminary preprocessed data in NPY format. Further data processing, such applying sliding window, is detailed in Section 3.2.
3.2 DATASET CURATION AND WORKFLOW
Figure shows a comprehensive data processing and workflow for combustion dynamics physi- cal field dataset. Initially, we use Python tool to handle the data preprocessing, including cleaning and formatting to enhance data quality and adaptability. Subsequently, the process employs foun- dational visual models, opatio-temporal models, and neural operators for in-depth data processing and feature extraction. These models train within the PyTorch framework and save as checkpoints for storage and further use. Finally, the workflow uploads these model checkpoints to GitHub for sharing and version control, enabling model loading for further inference and applications. During the data pre-processing phase, shown in Appendix (see Figure 8), the process uses the Numpy library in PyTorch for numerical computations. It loads data from multiple files and concate- nates it along specified axis. The process defines sliding wiiiddwwiiii function using the numpy. lib. sstride www.edeeiiiiiii tricks. as stridedd method to generate sliding window view of the data. This method alters the array's 's strides to view different parts of the array without copying the original data, ideal for time series analysis. It captures time-dependent characteristics effectively and facilitates further analysis and model training, particularly when considering the data's temporal continuity and local features.
3.3 DATA ANALYSIS & CHARACTERISTICS
Figure 4(a) shows the distribution of temperature, velocity, and pressure and their changes. The temperature distribution shows most values are low with big right skew. Over time, the temperature goes up. peaks, and then stays stable. The velocity distribution shows most values are low, and the time series shows a slow rise, leveling off later. The pressure distribution shows most values are in the middle with some extremes, and the time series shows a quick drop at first, then stability. Overall, these variables show clear phases time, reflecting the system's 's initial changes and final stable state. This highlights the advantages of our Open-CK dataset, which encompasses multiple physical variables. Figure 4(b) shows the t-SNE clustering results for temperature, velocity, and

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Temporrtur Mirritutton
Terpeestur Serie
Terpentai
Weddy
Fresue
20.2
100
(4)
380
109
100
Vilccty wacchyTraaaal Sel
102 TO 300 400
1.0
200
Presure Derbettee
Dramurs Tims Sette
2000
Poneuue Pi]
(4)
(b)
Figure 4: (a) Distribution and time series of temperature, velocity, and pressure. (b) I-SNE clustering results
for temperature, velocity, and pressure.
Table 2: Baseline results across the Open-CK dataset. We make the best performance baseline by using
and denote spatio-temporal, vision and neural operator backbones, respectively.
Method
Params (M)
FLOPs (G)
FPS
MSE 1
MAE
SSIM 1
PSNR
ConvLSTM Shi et al. (2015)
17.2301
60,4823
109.8573
0.0307
92.1234
0.9271
21.8497
PredRNN Wang al. (2017)
25,3652
119.9854
53.2781
0.0243
73.9876
0,9445
23.0932
PhyDNet Guen Thome (2020)
35,9784
169,9863
40.1256
0.0224 O
70.0154
0,9502
23.7154 O
MIM Wang et (2019)
39.0347
179.9743
36.2458
0.0232
70.4567
0,9478
23.4789
PredRNNv2 Wang et (2022b)
50.0789
399.9823
16.9832
0.0364
80.0234
0,9296
21.0032
PastNet Wu al. (2023c)
4,0023
15.9874
179.8764
0.0282
79.0145
0,9363
22.4876
SimVP Gao ct al. (2022a)
4.9873
17.9856
200.2345
0.0271
78.4967
0.9375
22. 22.6045
SimVPv2 Tan et al. (2022)
23.9765
117.9823
50.7896
0.0246
73.9872
0.9448
23.2012
Earthfurseer Wu et al. (2024a)
24.0123
118.1234
51,0987
0.0245
73.9234
0,9446
23.1987
ResNet He et al. (2016)
60.0987
19.9876
209.8765
0.0321
99.9997
0,9248
21.7967
U-Net Ronneberger et (2015)
45.0987
17.2345
279.9876
0.0252 O
72.0034
0.9463
23.1984
VIT Dosovitskiy ct al. (2020)
47.0012
17.0987
280.1234
0.0273
77.9823
0.9401
22.7989
Swin Transforner Liu et al. (2021)
46.9876
16.9874
290.2345
0.0362
96.1456
0.9137
21.5987
MLP-Mixe Tolstikhin al. (2021)
47.0543
16.9845
289.9876
0.0359
96.0765
0.9143
21.5984
FNO et al. (2020)
51,0987
19.2345
189.8765
0.0298
79.5432
0,9364
22.2834
LSM Wu al. (2023a)
$9.0012
19.9987
209.8765
0.0323
89.5432
0,9249
21.7965
CNO Raonic ct al. (2024)
45.0001
17.0032
280.7654
0.0251 O
72.0123 O
0,9458
23.2087 O
UNO Ashiqur Rahman et al. (2022)
47.9765
18.0012
280.6543
0.0274
78.0876
0.9402
22.7896
NMO Wu al. (20244)
46.9876
17.1098
290.7654
0.0361
95.9345
0.9142
21.6012
pressure. Temperature data points cluster closely in several distinct groups. Velocity and pressure data points display more complex and dispersed patterns, indicating more diverse variations in these variables over space time.
3.4 OPEN-CK LICENSE
The LargeST benchmark dataset is released under a CC BY-NC 4.0 International License: https: //creat t vecommons. wtggliiin censess /bw-nc//.nnnn. Our code implementation is released under the MIT License: https: / optpsourceeceece org/ / censes /MIT. The license of any specific baseline methods used in our codebase should be verified on their official repositories.
4 EXPERIMENT AND ANALYSIS
4.1 EXPERIMENTAL SETTINGS
Dataset. We select partial data for our main experiment. Specifically, with heat release rate of 5MW, a single fire source, and one wind direction, we simulate wind speeds of I mm/s, 2m/s, 3m/s, 4m/s, and 5m/s. Using the 12 fire growth model with a growth coefficient of 0.178, we calculate the time to reach steady-state fire as 167.6 seconds. Thus, choose simulation duration of 300 seconds for these conditions. Through numerical simulation, we obtain temperature data during the fire evolution, which
as the original training and testing dataset.
Baselines & Implementation Details. We select representative models from three domains as baselines. Spatio-temporal Predictive Models. We feature ConvLSTM Shi et al. (2015), PredRNN Wang et al. (2017). PhyDnet Guen & Thome (2020), MIM Wang et al. (2019), Pre- dRNNv2 Wang al. (2022b), PastNet Wu al. (2023c), SimVP Gao al. (2022a), Vim Tan

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Time stepa
Time steps 100s, 300l
Time steps 10lls, 300l
Presure Veindiy
Ground Truth
Earthfurseer
SimVPv2
Figure 5: Comparison of predicted and true values of temperature, velocity, and pressure fields different time steps (Is, 100s, 200s, 300s).
et al. (2022), Earthfarseer Wu et al. (2024a). D Vision Backbone. We include ResNet He et al. (2016), U-Net Ronneberger et al. (2015), Vision Transformer((iT] Dosovitskiy et al. (2021), Swin Transforme(SWIINT) Liu et al. 1) and MLP-Mixer Tolstikhin et al. (2021). & Operator Learn- ing Frameworks. We cover FNO Li et al. (2020), LSM Wu et al. (2023a), UNO Ashiqur Rahman et al. (2022), CNO Raonic et al. (2024), and NMO Wu et al. (2024d). Baseline details can be found in Appendix B. All backbones in this paper train with MSE loss, & Ba (2014), and set the learning rate to 10 13-3, The batch size is 50, and training early stops within 500 epochs. We train on 64 NVIDIA 40G-A100 GPUs. More details settings are left in Appendix C.
the ADAM optimizer Kingma
Metrics Details. We comprehensively evaluate the performance supported models in the above tasks using different metrics based on task characteristics: Error Metries: We use Mean Squared Error (MSE) and Mean Absolute Error (MAE) to measure the difference between predicted results and true targets. For weather forecasting, typically use Root Mean Squared Error (RMSE). Simi- larity Metrics: We use Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) to assess the similarity between predicted results and true targets. These metrics widely used in image processing and computer vision. Perceptual Metries: We use Learned Perceptual Image Patch Similarity (LPIPS) evaluate the perceptual difference between predicted results and true targets in the visual system. LPIPS aligns with human visual perception and is suitable for fire video prediction tasks. Physical Metrics: We use Energy Spectrum Error evaluate the physical consistency of deep learning models by calculating the energy spectrum error of the velocity field, assessing the model's performance in capturing fluid dynamics features. Computational Metries: We assess the computational complexity of the model through the number of parameters and Float- ing Point Operations (FLOPs). Additionally, we report the frames per second (FPS) on a single NVIDIA A100 GPU to evaluate inference speed.
4.2 MAIN RESULTS
As shown in Table 2, the bascline results on the Open-CK dataset show various models perform- ing differently across evaluation metrics. In spatio-temporal prediction models, PhyDNet excels with the lowest MSE of 0.0224 and MAE of 70.0154, indicating high accuracy in fire dynamics prediction. It also achieves the best SSIM of 0.9502 and PSNR of 23.7154, showing significant advantages in capturing complex spatio-temporal dynamics. PredRNN and SimVP have better FPS 53.2781 and 200.2345, respectively, but their error metrics are as strong. In vision back- bones, U-Net performs best with MSE of 0.0252, MAE of 72.0034, SSIM of 0.9463, and PSNR of 23.19844 demonstrating excellent capability in high-resolutie ViT show high FPS at 209.8765 and 280. 1234 but do not match U-Net in eтror metrics. In neural operator frameworks, CNO stands out with MSE of 0.0251. MAE of 72.0123, SSIM 0.9458, and PSNR of 23.2087, proving its effectiveness in solving PDE problems. FNO and UNO also perform well computational efficiency and some error metrics but are slightly inferior to CNO overall. These results indicate different models have unique strengths in handling combustion dy- namics data. Spatio-ta mentio-tempora models like PhyDNet excel in accuracy and dynamic changes. Vision backbones like U-Net are outstanding in image processing. Neural operators like CNO show signifi- cant effectiveness in PDE solutions. This analysis provides a solid basis for optimizing and selecting backbones for specific tasks.
image processing. ResNet and
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Additionally, the qualitative analysis results shown in the Figure 5, comparing the predicted and true values of temperature, velocity, and pressure fields at different time steps (Is, 100s, 200s, 300s). The temperature field predictions are accurate at the initial time (Is) for all models, but Earthfarseer and Sim2Pvv show significant deviations at 200s and 300s, especially at the boundaries of high- temperature regions. Velocity field predictions large errors in the later stages (200s and 300s), with poor handling of high-velocis details. Pressure field predictions are accurate initially but have significant errors high-pressure regions at 200s and 300s. Overall, Earthfarseer and VPv2 need improvement in long-term predictions and complex scenarios.
good in the early stages (Is and 100s) but show
4.3 PHYSICAL CONSISTENCY ANALYSIS
Ground-Truch
L-Ner Prediction
Time secies
Voladdly sremalized
15
10
Eartufissess Prediction
C Net Predictos
110
2015
9001
4m
350
4007
Time Stcp
Time Step
(b)
Figure Visualization U-Net and Earthfarseer model performance in predicting velocity fields. (a) The first row shows ground-truth and U-Net predictions, indicating high accuracy. The second row shows energy spectra, with U-Net closely matching the true spectrum but diffcring in high-frequency regions. (b) Time series plots of normalized velocity at arbitrary coordinates for all time steps show Earthfarseer and U-Net predictions align well with the ground truth. U-Net demonstrates slightly better consistency and accuracy in maintaining velocity trends.
Because the Open-CK benchmark has physical properties, we analyze its physical consistency Wu et al. (2024d); Wang et al. (2020) across different backbones in this section. The Figure 6 shows the performance of U-Net and Earthfarseer models in predicting velocity fields. In the first row of sub- figure (a), ground-truth, indicating high accuracy. The second row shows the energy spectra. While U-Net's predicted energy spectrum is close to the true spectrum, there are some differences in the high- frequency region, indicating U-Net's slight deficiency in capturing fine structural changes. Subfig- (b) shows normalized velocity time series arbitrary coordinates for all time steps. Earthfarseer and U-Net predictions align well with the ground truth. U-Net shows slightly better consistency and accuracy in maintaining velocity trends over time. This analysis highlights U-Net's effective- ness in high-fidelity spatio-temporal predictions for dynamic systems while also indicating room for improvement high-frequency detail prediction.
the ground-truth and U-Net prediction results. U-Net's output closely matches the
4.4
CASE STUDY
In this section, we use temperature data from a three-source fire scenario for training and show relevant learning cases. Figure 7 shows the comparison of prediction results from Earthfarseer, U-Net, and FNO models with the ground truth. U-Net's predictions are very close to the ground truth, showing high accuracy. Earthfarseer and FNO capture the overall structure but have larger deviations in high-intensity areas. Table lists the quantitative evaluation metrics for each model, including SSIM, LPIPS, and MSE. U-Net achieves the highest SSIM (0.9332), indicating the best structural similarity. It also records the lowest LPIPS (9.8632) and MSE (0.0259), reflecting better perceptual quality and lower prediction error. Earthfarseer also performs well, with an SSIM of 0.9217, LPIPS of 10.022, and MSE of 0.0267. FNO captures the general trend but performs slightly worse SSIM (0.8977). LPIPS (1 (11.2837), and MSE (0.0357). This analysis highlights U-Net's superior performance in both visual and quantitative evaluations.

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Model
SSIM
LPIPS
MSE
Earthfarsoor
0.9217
10.022
0.0267
N-Net
0.9332
9.8632
0.0259
FNO
11.2887
0.0357
Table Quantitative evaluation metrics for Figure 7: Comparison of prediction results from Earthfarseer, U- Earthfarseer. U-Net, and FNO models, in- Net, and FNO models with the ground truth.
Groand-Truth
Earchtarweer
U-Net
FNO
cluding SSIM, LPIPS, and MSE.
5 FUTURE INSIGHT & LIMITATION
To advance the field of fire dynamics research, introduce the Open-CK as a new benchmark. This dataset integrates a total of 300 different fire scenarios simulated using high-resolution Com- putational Fluid Dynamies. It includes data from various physical parameters such as temperature, pressure, and velocity, providing a robust platform for comprehensive data analysis and modeling.
Enhanced Model Generalization through Multi-Physics Coupling. Our analysis suggests that combining data from multiple physical phenomena can significantly improve the prediction accuracy and robustness of fire dynamics models. Future research could leverage this multi-physics dataset to develop models that provide deeper insights into the complex interactions within fire dynamics, enhancing both predictive power and real-world applicability.
A Testbed for Non-linear and Chaotic Combustion Dynamics. Open-CK captures wide range of non-linear and chaotic behaviors in fire scenarios, making it an excellent resource for testing new theories and models that address rapid changes in environmental conditions. This is especially valuable for developing strategies to predict and mitigate sudden fire escalations.
Development of Real-Time Predictive Models. Open-CK's s extensive range and diversity provide an excellent foundation for developing real-time predictive models. This could potentially forecast the progression fire and other dynamic phenomena they happen, allowing for timely interven- tions.
While Open-CK is a powerful benchmark for fire dynamics research, it is not without its limitations. Firstly, Open-CK mainly uses simulated data that might not capture all the unpredictable changes seen real-world fire events. Additionally, the accuracy of the simulations depends heavily on the fidelity of the input parameters and the resolution of the data, which can be compromised by computational limitations and sensor inaccuracies.
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A INTRODUCTION TO PSEUDO ALGORITHMS
import numpy as up
from numpy. lib stride import as-strided
file_ _nmmes 1'400x300x300 np. -400x30xx300-2. -2.
4400330x3000 npy 44003300x000 npy
400x330x3000 npy
names data = up. load [ [0])
10 for fale-name fale-names [1:]:
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next-data = np load
load (file. .nmme)
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data = np. concatenate ((data, next_data), axis=0)
def sliding window view (arr, window size, step=l):
15
arr. shape [0]
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return as as-strided (arr,
shape=(n window + 1. handow.sszee arr shape [1:], strides=(an) arr. strides[0] step arr strides)
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window _size = 80
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step =
sliding data sliding window view (data, window size, step)
Figure 8: Example of Python code for processing multiple Numpy tiles and creating a sliding window view of the data.
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60m
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Figure 9: top-view
20m
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Figure 10: side-view
10m
Figure 11: back-view
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Figure 12: The figure shows the design interface of Pyrosim software, displaying the factory scenario used in this study.
10.0
22.29
25.50
160
Figure 13: This figure demonstrates the use of the PyrosimSimResult feature in the Pyrasim software to visualize the results of numerical simulations.
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def merge_fds-files (oriiiiil (original_file, output_file): Read the original file content
with open ( (original.fill .flle. 'r') as f:
original.content = readlinees
Find the index of the line containing &SURE ID= fire surf_index None
for i. line in enumerate ( (original
if "&SURF ID=' fire "," in line
surf_index = break
11
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if surf index is None:
print("Ling containing &SURF ID=' fire' not found") return
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# Save the new parameters
new content [] Sensor spacing
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dist =
21
startx 0.0
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starty 0.0
# Sensors fixed at height of 5m plane
24
z
25
26
# Add temperature field sensors
sum 0
for in (a0g))300):
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for j range (300):
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x = round (startx + dist, 1) y round + dist 1)
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+=
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QUANTITY= new.content. cew_connent.appeedd ("&DEVC ID=" + f" (emperrrtuur (sum)), "/" + "[n")
"TEMPERATURE" XY7=" + sxr(x) "," + str + str (z)
new content. a"(pn)
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Add velocity field sensors
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# Add pressure sensors
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# Insert the new content at the specified position in the original content
conten original original.content surf new.content [
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# Write the merged content to new file
with open ( (output_file. 'w') as f:
f. writelines (merged content)
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print MeMer completed, result saved to", output file)
Figure 14: Pseudocode for processing an fds file and adding sensor data in Python.
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Published as a conference paper at ICLR 2025
import numpy np
import pandas pd
CSV file paths
csv file path = -'110NN-31-2wdir-- deve csv 1100--1--1----- deve .
WWN-33--2wirr-3-deee csv 1 -10 .deve..cvv)
Paths to save the npy files
ppy__fll__ppp ffll.-iaahhhhhhe = [ [00N----22-00033::003300---33113 npy
x3x300x300 np. 1100--T-22-400333330000--5--221 11M-333-20033-02
x3x300x300 np.
# Clean and filter each CSV file for in range range(Ien (len(es) (ss.fficc.pahh)
10
# Read the CSV file
12
df pd. read_esv (eadd.ss(es..iile.pppp (esv.file.path [i])
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# Drop the first column
15
df df. drop df. columns [0], axis=l)
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Drop the first two rows df dr. iloc [2:] iloc is used for row-based slicing
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# Convert all data to float type
df df. astype
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# Convert the DataFrame to a NumPy array data = df. values
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Define length of each sub-column sub_column.length 300
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# Current dimensions Target dimensions are (T, dtype. row, col)
400x27000 (T=dnum)
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Reshape data to (400, 3. 300. 300)
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reshaped_data data reshape (400, 3, sub_columm.length, -1)
print (reshaped data shape)
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Save as npy file np- save npy file.flllee path [i], reshaped_data)
37
Figure 15: Pseudocode for filtering and cleaning CSV file, transforming its dimensions, and finally saving it uny file in Python.
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Published as a conference paper at ICLR 2025
(m/
Velocity
TTm
50s
100s
150s
200s
Figure 16: The figure illustrates evolution of the fire under the conditions a heat release rate of MW. three ignition sources, and a single wind direction with a wind speed I m/s. The fire growth coefficient set to 0.8.8. Each frame in the sequence is separated by an interval of 50 seconds.
Vel (m/+)
Tom (C)
Camperatie
(Ph)
50s
100s
150s
200s
Figure 17: The figure illustrates the evolution of the fire under the conditions a heat release of MW. three ignition sources, and a single wind direction with a wind speed 2 m/s. The fire growth coefficient set to 0.1.8. Each frame in the sequence is separated by an interval of 50 seconds.
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Published as a conference paper at ICLR 2025
Vel (m/
Velocity
Pressure Temperature
(Pr)
50s
100s
150s
200s
Figure 18: The figure illustrates evolution of the fire under the conditions a heat release rate of MW. three ignition sources, and a single wind direction with a wind speed 3 m/s. The fire growth coefficient set to 0.8.8. Each frame in the sequence is separated by an interval of 50 seconds.
Vel (m/+)
Velocity
(C)
Temperature
Pressure
50s
100s
150s
200s
Figure 19: The figure illustrates the evolution of the fire under the conditions heat release of MW. three ignition sources, and a single wind direction with a wind speed 4 m/s. The fire growth coefficient set to 071. Each frame in the sequence is separated by an interval of 50 seconds.
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Published as a conference paper at ICLR 2025
Velocity
Tem (C)
Temperature
Pressure
50s
100s
150s
200s
Figure 20: The figure illustrates evolution of the fire under the conditions a heat release rate of MW. three ignition sources, and a single wind direction with a wind speed 5 m/s. The fire growth coefficient set to 0.1.8. Each frame in the sequence is separated by an interval of 50 seconds.
Vel (m//4)
Velocity
Tem (C)
Pres (Pa)
Pressure
30s
60s
90s
120+
Figure 21: This figure shows the evolution of the fire under the conditions of heat release of MW.
three ignition sources, and two wind directions with a wind speed of m/s. The fire growth coefficient set to 0.178. Each frame in the sequence is separated by an interval of 30 seconds.
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Published as a conference paper at ICLR 2025
(m1
Velocity
Tem (C)
Pressure Temperature
30s
60s
90s
120s
Figure This figure shows the evolution of the fire under conditions a heat release rate of MW. three ignition sources, and two wind directions with a wind speed of 2 m/s. The fire growth coefficient set to 0.178. Each frame in the sequence is separated by an interval of 30 seconds.
Vel (m/s)
Velocity
(C)
Temperature
(Pa)
30s
120s
Figure 23: 1'his figure shows the evolution of the fire under the conditions of heat relcase of MW. three ignition sources, and two wind directions with a wind speed of m/s. The fire growth coefficient set to 0.178. Each frame in the sequence is separated by an interval of 30 seconds.
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Published as a conference paper at ICLR 2025
Vel (m/s)
Velocit
(C)
Pressure Temperature
30s
60s
90s
120g
Figure This figure shows the evolution of the fire under the conditions a heat release of MW. three ignition sources, and two wind directions with a wind speed of 4 m/s. The fire growth coefficient set to 0.178. Each frame in the sequence is separated by an interval of 30 seconds.
Vel (m/6)
Velocity
Tem
Pres (Pa)
Pressure
30s
60s
90s
120s
Figure 25: This figure shows the evolution of the fire under the conditions of a heat release of 10 MW. three ignition sources, and two wind directions with a wind speed of m/s. The fire growth coefficient set to 0.178. Each frame in the sequence is separated by an interval of 30 seconds.
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Published as a conference paper at ICLR 2025
Table 4: Baseline Model Configurations. This table summarizes the configurations of various baseline models, including their hidden dimension, number layers, learning rate, dropout rate. These models are used to handle spatiotemporal sequence data efficiently, with different configurations affecting their complexity, convergence speed, and overfitting prevention.
Model
Hidden Dim
Layers
Learning Rate
Dropout Rate
ConvLSTM
128
3
0.001
0.2
PredRNN
256
4
0.001
0.3
PhyDNet
128
5
0,0005
0.2
MIM
256
0.001
0.2
PredRNNv2
256
0.0005 6
0.3
PastNet
128
0.001 3
0.2
SimVP
128
0.001 3
0.2
Sim2
128
0.0005
0.2
Earthfarseer
256
0.0005 5
0.3
ResNet
256
0.0001 6
0.2
U-Net
128
0.001 5
0.3
ViT
256
0.0001 6
0.2
Swin Transformer
256
0.0001 6
0.2
MLP-Mixer
256
0.0005 4
0.2
FNO
128
0,001 3
0.2
LSM
128
0.001 3
0.2
CNO
128
0.001
0.2
UNO
256
5
0,0005
0.2
NMO
256
5
0.0005
0.2
B BASELINE DESCRIPTIONS
ConyLSTM Shi al. (2015): Combines convolutional neural networks and LSTM for spatiotem- poral sequence prediction, suitable for handling spatiotemporal data.
PredRNN Wang et al. (2017): Uses recurrent neural network with multi-level recurrent struc- ture and cross-memory cells to improve spatiotemporal feature modeling.
PhyDNet Guen & Thome (2020): Combines explicit physical constraints with implicit neural
network models for video prediction, enhancing prediction accuracy.
MIM Wang al. (2019): Utilizes memory-in-memory mechanism for video prediction, captur- ing complex spatiotemporal patterns.
PredRNNv2 Wang et al. (2022b): An improved version of PredRNN with a deeper recurrent
structure and more complex memory units for better spatiotemporal prediction performance.
PastNet Wu et al. (2023c): Uses past time step data for prediction, with efficient spatiotemporal prediction architecture achieving good performance with fewer parameters.
SimVP Gao et al. (2022a): A spatiotemporal prediction model based on simple visual transforma- tions, featuring high computational efficiency.
VPv2 Tan et al. (2022): An improved version of SimVP with optimized visual modules and additional feature transformation layers, enhancing prediction performance.
Earthfarseer Wu et al. (2024a): Designed for earth system prediction, combining various spa- tiotemporal feature extraction methods to improve accuracy in earth science predictions.
ResNet He al. (2016): A deep residual network that addresses the vanishing gradient problem in deep networks, widely used in image recognition and classification tasks.
U-Net Ronneberger et al. (2015): A convolutional neural network- image segmentation model with encoder-decoder structure, widely used in medical image processing and spatiotem- poral prediction tasks.
ViT Dosovitskiy et al. (2020): A Vision Transformer model that captures long-range dependencies in images through the self-attention mechanism.
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Swin Transformer Liu et al. (2021): An improved Transformer model with hierarchical window attention mechanism for processing high-resolution images.
MLP-Mixer Tolstikhin al. (2021): A fully connected neural network that performs image clas-
sification by mixing features and positional encodings, simplifying the design of traditional convo- lutional networks.
FNO Li et al. (2020); A Fourier Neural Operator model for solving partial differential equations using Fourier transforms to improve computational efficiency.
LSM Wu et al. (2023a): Uses state-space models for spatiotemporal prediction, learning state transition and observation equations for high-precision forecasting.
CNO Raonic al. (2024): Combines convolutional neural networks and neural operator models to capture local spatiotemporal features for efficient PDE solving.
UNO Ashiqur Rahman et al. (2022): A U-shaped memory enhanced architecture that allows for deeper neural operators.
NMO Wu et al. (2024d): An operator learning paradigm for learning the intrinsic dimension rep- resentation of the underlying operator.
C BASELINE MODEL CONFIGURATIONS
This section describes the baseline models and their configurations, including model name, hidden dimension, number of layers, learning rate, and dropout rate. Table summarizes the configurations for each model.
The models include traditional convolutional neural networks, recurrent neural networks, and recent Transformer models. These models handle spatiotemporal sequence data efficiently. The hidden dimension and number of layers determine model complexity, the learning rate affects convergence speed, and the dropout rate helps prevent overfiuting. Comparing these baseline models' perfor- mance provides insights into their effectiveness for specific tasks and guides further research.
D LIMITATIONS AND FUTURE WORK
D.1 LIMITATIONS
Although the dataset considers various fire scenarios and environ- mental variables, real-world fires often complex, with greater diversity and un- predictable variations. For example. differences in structural features, building materials, and crowd density may not be fully represented in the dataset.
Scenario Limitation:
Model Generalization: Since the dataset is generated under controlled conditions, the model may struggle to handle the complex and dynamic fire scenarios encountered in the real world. Actual fires may involve different fire sources, combustible materials, and changing building structures, which could fall outside the scope of the current dataset.
Environmental Factors: Real-world fire development is influenced by many uncontrol- lable factors, such as climate conditions, weather changes, and evacuation situations. These factors might not have been fully considered in the simulation, leading to reduced predic- tion accuracy when the model applied to actual fire scenarios.
D.2 FUTURE WORK
To adapt model pre-trained on the Open-CK dataset for real-world fire dynamics modeling, the following measures can be taken:
Data Augmentation: Introduce more real-world fire scenario data to enhance the model's generalization ability. For example, by incorporating actual fire records and incident data, the deficiencies of the Open-CK dataset can be addressed, adding diversity to the scenarios.
Transfer Learning: Pre-train the model on the Open-CK dataset and then fine-tune fire dataset that more closely resembles real-world conditions. This approach allows
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Published as a conference paper at ICLR 2025
the model to retain the fundamental fire dynamics features learned from Open-CK while adapting to new environments and scenarios.
27


Reviews:
[{"review_id": "2xya76jxtD", "reviewer": "Reviewer_V722", "summary": "This paper details a new dataset created using FDS for industrial fire parks. The authors use various state of the art SciML techniques on the dataset to show it's usefulness.", "questions": "as per above", "limitations": "", "rating": 5, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "There is such a lack of data within the AI4Science domain, and in particular for fluid dynamics and fire modelling that any additional, well produced data is a strength. Clearly a lot of work has been undertaken to produce the data, which is a credit to the authors.", "weaknesses": "I'm afraid there are a number of weaknesses that makes this work, at present, not suitable for publication/presentation at ICLR:\n\n1) Lack of any verification or validation of the underlying FDS methodology/mesh/technique for the simulated domain. \n2) there are some phrases in the document sound odd i.e the use of the word supercomputers in italics. It sounds a little odd and is repeated in several places\n3) no discussion on how the data will be maintained/stored/accessed\n4) limited discussion of the limitations of the work\n\nOverall, not high enough quality at present but I would encourage the authors to revisit and improve the paper for future conferences/publications.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "bgJouKznSN", "reviewer": "Reviewer_mR8y", "summary": "This is a strong work with good dataset and ML evals for an important application. The paper is written well. There is good documentation and open practices -- well done.\n\nHowever, the language and claims can come across a bit strong for a scientific paper -- see questions. I can confidently recommend this paper for acceptance -- if my concerns in questions are addressed.\n\nEdit 1: Questions and concerns have been addressed. Changing score to 8.", "questions": "1. Is OpenCK the first combustion CFD benchmark? This is a strong statement. For example, Sandia's Engine Combustion Network by Pickett,Payri et al has been providing open data and benchamrking with regards to gasoline and diesel CFD since about 9 years ago. Another similar effort to this is BLASTNet in Chung et al (NeurIPS 2023) which involved Direct Numerical Simulation data of canonical combustion configurations.  Please revise this statement to be more moderate.\n2. \"Open-CK involves several PDEs\" -- All listed PDE's are actually just scalar/vector conservation equations. Is this statement really true?\n\n3. It would be interesting to see if the effects of model scaling has metrics in table 2. Bigger,expensive models tend to outperform smaller models. What does MSE vs FLOPS or MSE vs Params, SSIM vs (FLOPs, Params) look like in a scatter plot? This can provide more insight into useful architectures.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "1. Good motivation and review of related work\n2. Impressive dataset covering many scenarios and physical quantities with expensive compute.\n3. Good eval with many architectures.\n4. Open Repository\n5. Good future insight and limitations discussion\n6. Good documentation.", "weaknesses": "1. Language is a bit bold for some claims.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "vvpBYZxUUW", "reviewer": "Reviewer_HDdZ", "summary": "This is data set and benchmarking paper that uses the Fire Dynamics Simulator (FDS) to create a Combustion Kinetics (CK) dataset for SciML research. It includes various physical fields such as temperature and pressure, and covers multiple environmental combinations for exploring multi-physics field coupling phenomena. The authors evaluate SOTA ML architectures to establish an Open-CK benchmark.", "questions": "1. Provide better description of the sample size in the data set. How does the '300 different fire scenarios' connect with the details provided in Table 1?\n\n2. Can the data set be enriched by adding diverse layouts for the industrial park scenario? e.g., different number oil storage areas, other geometrical constructions/objects", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. This is an interesting and potentially useful data set for SciML research, the authors made substantial computational effort to create the data set and establish the ML benchmark - the motivation of the work and description of the contributions are nicely laid out. \n2. The consideration of multiphysics simulations, particularly over complex boundaries/plant layouts is interesting and could lead to interesting SciML models and applications. \n3. The use of LPIPS metric and other natural image based metrics to evaluate the models is quite intriguing.", "weaknesses": "1. From the paper, it is not very clear how many samples are actually in the dataset - the authors mention that there are '300 different fire scenarios' (I could not open the Project website link that was provided in the paper - not sure if it's a problem on the server side or on my side). So, are there just 300 time series samples of different lengths across various combustion parameters and environmental conditions?\n\n2. If I understand correctly, the paper considers only one geometrical layout for the data set. While the setup represents a typical industrial park scenario, the data set may not be rich enough for generalizable SciML research without having diverse geometrical layouts.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "iSf0gc2XJx", "reviewer": "Reviewer_f2LF", "summary": "This paper unveils a novel benchmark dataset for improved modeling of combustion kinetics (CK) using data-driven techniques. Specifically, the dataset simulates the development of fires in industrial parks using computational fluid dynamics simulations using the fire dynamics simulator. In the paper, the authors detail that the generated dataset comprises 300 different scenarios of fire development all emanating from a single ignition source (SIS) or three ignition sources (TIS). In addition to the dataset, authors also conduct extensive experiments using state of the art scientific machine learning baselines to establish a research benchmark for modeling fire development, a critical and challenging problem. This dataset will fill a critical need, serving to accelerate the modeling of fire development using data-driven techniques.", "questions": "1. How were each of the 300 scenarios conceived and selected for simulation? How representative are these 300 scenarios of real-world fire development dynamics?\n\n2. What might be the possible deficiencies of models trained on the current datasets in predicting real-world fire development dynamics? Specifically, how can a model pre-trained on the Open-CK dataset be adapted to real-world fire dynamics modeling?\n\n3. What are some existing popular theoretical models (reduced-order or otherwise) that are employed to estimate fire development dynamics, how do data-driven models compare to these models w.r.t physical consistency and estimation accuracy?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "1. The dataset is necessary and crucial. As pointed out by the authors, other datasets for fire development usually target satellite images and are in the context of wildfires or have low spatial and temporal resolution. \n\n2. The set of scientific machine learning models employed to evaluate performance on the developed dataset is comprehensive, setting up a strong research benchmark.\n\n3. The rigorous experimental evaluation is well detailed and has been carried out with vision backbones (e.g., U-Net, ViT, ResNet), scientific machine learning backbones (e.g., Fourier Neural Operator) as well as spatio-temporal backbones (e.g., ConvLSTM).", "weaknesses": "1. Currently, the narrative lacks detailing of the domain background of the combustion kinetics field. For the reader to fully appreciate the comprehensive nature of the benchmark, a more thorough description of the problem and the generated dataset is necessary. Specifically, a reading of the current version of the paper does not leave the reader with a sense of how representative the current Open-CK dataset is of real-world single-source (or multi-source) fires in industrial contexts?\n\n2. A more thorough description of the design decisions made to select the various scenarios is necessary. These scenarios are summarized in Table 1 but a detailed description is lacking of why each of these scenarios is important, representative o real-world scenarios and challenging to model. Without this, it is hard to truly appreciate the extent of the contribution of this dataset to the field of combustion kinetics.\n\n3. Finally, a better motivation about exactly why modeling the physical coupling between the multiple physical fields is challenging and crucial is necessary to fully understand the richness and impact of the current dataset in the CK context.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)