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This work presents a large language model (LLM)-based agent OpenFOAMGPT tailored for OpenFOAM-centric computational fluid dynamics (CFD) simulations, leveraging two foundation models from OpenAI: the GPT-4o and a chain-of-thought (CoT)-enabled o1 preview model.
H. G. Weller, G. Tabor, H. Jasak, and C. Fureby, A tensorial approach to computational continuum mechanics using object-oriented techniques, Computers in physics 12
1998
Earlier work this paper cites.
J. Ling, A. Kurzawski, and J. Templeton, Reynolds averaged turbulence modelling using deep neural networks with embedded invariance, Journal of Fluid Mechanics 807
2016
Earlier work this paper cites.
X. Chu, E. Laurien, and D. McEligot, Direct numerical simulation of strongly heated air flow in a vertical pipe, International Journal of Heat and Mass Transfer 101
2016
Earlier work this paper cites.
S. Pandey, X. Chu, and E. Laurien, Investigation of in-tube cooling of carbon dioxide at supercritical pressure by means of direct numerical simulation, International Journal of Heat and Mass Transfer 114
2017
Earlier work this paper cites.
J. Wu, H. Xiao, and E. Paterson, Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework, Physical Review Fluids 3
2018
Earlier work this paper cites.
W. Chang, X. Chu, A. Fareed, S. Pandey, J. Luo, B. Weigand, and E. Laurien, Heat transfer prediction of supercritical water with artificial neural networks, Applied Thermal Engineering 131
2018
Earlier work this paper cites.
S. Pandey, X. Chu, E. Laurien, and B. Weigand, Buoyancy induced turbulence modulation in pipe flow at supercritical pressure under cooling conditions, Physics of Fluids 30
2018
Earlier work this paper cites.
K. Duraisamy, G. Iaccarino, and H. Xiao, Turbulence modeling in the age of data, Annual Review of Fluid Mechanics 51
2019
Earlier work this paper cites.
A. Beck, D. Flad, and C. Munz, Deep neural networks for data-driven LES closure models, Journal of Computational Physics 398
2019
Earlier work this paper cites.
X. Yang, S. Zafar, J.-X. Wang, and H. Xiao, Predictive large-eddy-simulation wall modeling via physics-informed neural networks, Physical Review Fluids 4
2019
Earlier work this paper cites.
X. Chu, G. Yang, S. Pandey, and B. Weigand, Direct numerical simulation of convective heat transfer in porous media, International Journal of Heat and Mass Transfer 133
2019
Earlier work this paper cites.
S. Pandey, J. Schumacher, and K. R. Sreenivasan, A perspective on machine learning in turbulent flows, Journal of Turbulence 21
2020
Earlier work this paper cites.
C. Evrim, X. Chu, and E. Laurien, Analysis of thermal mixing characteristics in different t-junction configurations, International Journal of Heat and Mass Transfer 158
2020
Earlier work this paper cites.
G. Yang, X. Chu, V. Vaikuntanathan, S. Wang, J. Wu, B. Weigand, and A. Terzis, Droplet mobilization at the walls of a microfluidic channel, Physics of Fluids 32
2020
Earlier work this paper cites.
P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W.-t. Yih, T. Rocktäschel, et al. , Retrieval-augmented generation for knowledge-intensive nlp tasks, Advances in Neural Information Processing Systems 33
2020
Earlier work this paper cites.
W. Wang, X. Chu, A. Lozano-Durán, R. Helmig, and B. Weigand, Information transfer between turbulent boundary layer and porous media, Journal of Fluid Mechanics 920
2021
Earlier work this paper cites.
C. Evrim, X. Chu, F. Silber, A. Isaev, S. Weihe, and E. Laurien, Flow features and thermal stress evaluation in turbulent mixing flows, International Journal of Heat and Mass Transfer 178
2021
Cited alongside, same era.
Y. Liu, A. Geppert, X. Chu, B. Heine, and B. Weigand, Simulation of an annular liquid jet with a coaxial supersonic gas jet in a medical inhaler, Atomization and Sprays 31
2021
Cited alongside, same era.
R. Vinuesa and S. L. Brunton, Enhancing computational fluid dynamics with machine learning, Nature Computational Science 2
2022
Cited alongside, same era.
W. Wang, A. Lozano-Durán, R. Helmig, and X. Chu, Spatial and spectral characteristics of information flux between turbulent boundary layers and porous media, Journal of Fluid Mechanics 949
2022
Cited alongside, same era.
2024
Later among the works it cites.
K. Chibwe, D. Mantilla-Calderon, and F. Ling, Evaluating gpt models for automated literature screening in wastewater-based epidemiology, ACS Environmental Au (2024)
2024
Later among the works it cites.
M. C. Ramos, C. Collison, and A. D. White, A review of large language models and autonomous agents in chemistry, Chemical Science (2024)
2024
Later among the works it cites.
B. Ni and M. J. Buehler, Mechagents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledge, Extreme Mechanics Letters 67
2024
Later among the works it cites.
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2022
Cited alongside, same era.
A. Beck and M. Kurz, Toward discretization-consistent closure schemes for large eddy simulation using reinforcement learning, Physics of Fluids 35
2023
Cited alongside, same era.
R. Vinuesa, S. L. Brunton, and B. J. McKeon, The transformative potential of machine learning for experiments in fluid mechanics, Nature Reviews Physics 5
2023
Cited alongside, same era.
2023
Cited alongside, same era.
B. Min, H. Ross, E. Sulem, A. P. B. Veyseh, T. H. Nguyen, O. Sainz, E. Agirre, I. Heintz, and D. Roth, Recent advances in natural language processing via large pre-trained language models: A survey, ACM Computing Surveys 56
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
G. Yang, R. Xu, Y. Tian, S. Guo, J. Wu, and X. Chu, Data-driven methods for flow and transport in porous media: a review, International Journal of Heat and Mass Transfer 235
2024
Cited alongside, same era.
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
D. Kim, T. Kim, Y. Kim, Y.-H. Byun, and T. S. Yun, A chatgpt-matlab framework for numerical modeling in geotechnical engineering applications, Computers and Geotechnics 169
2024
Later among the works it cites.
2024
Later among the works it cites.
Y. Liu, X. Chu, G. Yang, and B. Weigand, Simulation and analytical modeling of high-speed droplet impact onto a surface, Physics of Fluids 36
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
L. Contributors, Langchain (2024), gitHub repository
2024
Later among the works it cites.
A. Cremades, S. Hoyas, and R. Vinuesa, Additive-feature-attribution methods: a review on explainable artificial intelligence for fluid dynamics and heat transfer, International Journal of Heat and Fluid Flow 112
2025
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