MyCrunchGPT: A LLM Assisted Framework for Scientific Machine Learning

Journal Article · 02 October 2023 · Journal of Machine Learning for Modeling and Computing

DOI: https://doi.org/10.1615/jmachlearnmodelcomput.2023049518 · OSTI ID:2432500

Kumar, Varun ^[1]; Gleyzer, Leonard ^[2]; Kahana, Adar ^[2]; Shukla, Khemraj ^[2]; Karniadakis, George Em ^[2]

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1. Brown University, Providence, RI (United States); Brown University
2. Brown University, Providence, RI (United States)

Scientific machine learning (SciML) has advanced recently across many different areas in computational science and engineering. Here, the objective is to integrate data and physics seamlessly without the need of employing elaborate and computationally taxing data assimilation schemes. However, preprocessing, problem formulation, code generation, postprocessing, and analysis are still time- consuming and may prevent SciML from wide applicability in industrial applications and in digital twin frameworks. Here, we integrate the various stages of SciML under the umbrella of ChatGPT, to formulate MyCrunchGPT, which plays the role of a conductor orchestrating the entire workflow of SciML based on simple prompts by the user. Specifically, we present two examples that demonstrate the potential use of MyCrunchGPT in optimizing airfoils in aerodynamics, and in obtaining flow fields in various geometries in interactive mode, with emphasis on the validation stage. To demonstrate the flow of the MyCrunchGPT, and create an infrastructure that can facilitate a broader vision, we built a web app based guided user interface, that includes options for a comprehensive summary report. The overall objective is to extend MyCrunchGPT to handle diverse problems in computational mechanics, design, optimization and controls, and general scientific computing tasks involved in SciML, hence using it as a research assistant tool but also as an educational tool. While here the examples focus on fluid mechanics, future versions will target solid mechanics and materials science, geophysics, systems biology, and bioinformatics.

• Research Organization: Brown University, Providence, RI (United States)
• Sponsoring Organization: USDOE Office of Science (SC); Air Force Office of Scientific Research (AFOSR); Office of Naval Research (ONR)
• Grant/Contract Number: SC0023191
• OSTI ID: 2432500
• Journal Information: Journal of Machine Learning for Modeling and Computing, Journal Name: Journal of Machine Learning for Modeling and Computing Journal Issue: 4 Vol. 4; ISSN 2689-3967
• Publisher: Begell HouseCopyright Statement
• Country of Publication: United States
• Language: English