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Computer Science > Machine Learning

arXiv:2306.15551 (cs)
[Submitted on 27 Jun 2023]

Title:CrunchGPT: A chatGPT assisted framework for scientific machine
learning

Authors:Varun Kumar, Leonard Gleyzer, Adar Kahana, Khemraj Shukla,
George Em Karniadakis
Download a PDF of the paper titled CrunchGPT: A chatGPT assisted
framework for scientific machine learning, by Varun Kumar and 4 other
authors
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    Abstract: Scientific Machine Learning (SciML) has advanced
    recently across many different areas in computational science and
    engineering. 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
    CrunchGPT, 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 CrunchGPT 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 CrunchGPT, and create an
    infrastructure that can facilitate a broader vision, we built a
    webapp based guided user interface, that includes options for a
    comprehensive summary report. The overall objective is to extend
    CrunchGPT 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 in fluid mechanics, future versions will target
    solid mechanics and materials science, geophysics, systems
    biology and bioinformatics.

Comments: 20 pages, 26 figures
Subjects: Machine Learning (cs.LG); Computation and Language (cs.CL);
          Physics and Society (physics.soc-ph)
Cite as:  arXiv:2306.15551 [cs.LG]
          (or arXiv:2306.15551v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2306.15551
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Varun Kumar [view email]
[v1] Tue, 27 Jun 2023 15:23:42 UTC (16,721 KB)
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