# Can ChatGPT implement finite element models for geotechnical engineering applications?

arXiv: https://arxiv.org/abs/2501.02199 · HTML: https://mlsanity.com/paper/2501.02199/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 59 references that are in the map.
- First posted on arXiv 4 Jan 2025.

## Builds on (most cited first, 5 of 5)

- [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023)
- [MechAgents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledge](https://mlsanity.com/paper/2311.08166/) (2023)
- [On the Effectiveness of Large Language Models in Domain-Specific Code Generation](https://mlsanity.com/paper/2312.01639/) (2023)
- [Code Generation Tools (Almost) for Free? A Study of Few-Shot, Pre-Trained Language Models on Code](https://mlsanity.com/paper/2206.01335/) (2022)
- [ChatGPT for Programming Numerical Methods](https://mlsanity.com/paper/2303.12093/) (2023)

## Built on by (most cited first, 5 of 5)

- [A Large Language Model-Empowered Agent for Reliable and Robust Structural Analysis](https://mlsanity.com/paper/2507.02938/) (2025)
- [Domain adaptation of large language models for geotechnical applications](https://mlsanity.com/paper/2507.05613/) (2025)
- [Rethinking Scientific Modeling: Toward Physically Consistent and Simulation-Executable Programmatic Generation](https://mlsanity.com/paper/2602.07083/) (2026)
- [Physics-constrained symbolic regression for discovering closed-form equations of multimodal water retention curves from experimental data](https://mlsanity.com/paper/2603.03346/) (2026)
- [Automating structural reliability analysis with a multi-agent large language model framework](https://mlsanity.com/paper/2607.16580/) (2026)

## Abstract (arXiv)

This study assesses the capability of ChatGPT to generate finite element code for geotechnical engineering applications from a set of prompts. We tested three different initial boundary value problems using a hydro-mechanically coupled formulation for unsaturated soils, including the dissipation of excess pore water pressure through fluid mass diffusion in one-dimensional space, time-dependent differential settlement of a strip footing, and gravity-driven seepage. For each case, initial prompting involved providing ChatGPT with necessary information for finite element implementation, such as balance and constitutive equations, problem geometry, initial and boundary conditions, material properties, and spatiotemporal discretization and solution strategies. Any errors and unexpected results were further addressed through prompt augmentation processes until the ChatGPT-generated finite element code passed the verification/validation test. Our results demonstrate that ChatGPT required minimal code revisions when using the FEniCS finite element library, owing to its high-level interfaces that enable efficient programming. In contrast, the MATLAB code generated by ChatGPT necessitated extensive prompt augmentations and/or direct human intervention, as it involves a significant amount of low-level programming required for finite element analysis, such as constructing shape functions or assembling global matrices. Given that prompt engineering for this task requires an understanding of the mathematical formulation and numerical techniques, this study suggests that while a large language model may not yet replace human programmers, it can greatly assist in the implementation of numerical models.