mlsanityData to 14 Oct 2026

MyCrunchGPT: A chatGPT assisted framework for scientific machine learning

arXiv 2306.15551 ↗First posted 27 Jun 2023cs.LG cs.CL physics.soc-phcited by 8

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Abstract (arXiv)

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 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 webapp 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 in fluid mechanics, future versions will target solid mechanics and materials science, geophysics, systems biology and bioinformatics.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 29 of its 64 references that are in the map. First posted on arXiv 27 Jun 2023.

Builds on

29 of 64 references.

References · 29

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17 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 4; 2025: 2; 2026: 1.

8 papers.

  1. 2024Learning Agent-based Modeling with LLM Companions: Experiences of Novices and Experts Using ChatGPT & NetLogo Chatcited by 23
  2. 2025FEABench: Evaluating Language Models on Multiphysics Reasoning Abilitycited by 22
  3. 2025Toward Reliable Scientific Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Modelscited by 9
  4. 2023Enhancing Open-Domain Task-Solving Capability of LLMs via Autonomous Tool Integration from GitHubcited by 7
  5. 2024Explain Like I'm Five: Using LLMs to Improve PDE Surrogate Models with Textcited by 7
  6. 2024Adapting While Learning: Grounding LLMs for Scientific Problems with Intelligent Tool Usage Adaptationcited by 7
  7. 2024Using Large Language Models for Parametric Shape Optimizationcited by 7
  8. 2026Large language models for partial differential equation workflowscited by 0