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.
Elasto-plastic cell-based smoothed finite element method solving geotechnical problems
PowerChina Kunming Engineering Corporation Limited（中国水利水电昆明工程有限公司）；College of Water Conservancy and Hydropower Engineering, Hohai University（河海大学水利水电学院）
Nonlinear Geotechnical Analysis Using a Polygonal Cell-Based Smoothed Finite Element Framework
College of Water Conservancy and Hydropower Engineering, Hohai University（河海大学水利水电学院）；PowerChina Kunming Engineering Corporation Limited（中国电建昆明工程有限公司）；College of Harbour, Coastal and Offshore Engineering, Hohai University（河海大学港口海岸与近海工程学院）
Differentiate ChatGPT-generated and Human-written Medical Texts
South China University of Technology（华南理工大学）；University of Georgia（佐治亚大学）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Massachusetts General Hospital and Harvard Medical School（马萨诸塞总医院和哈佛医学院）
Large Language Models Streamline Automated Machine Learning for Clinical Studies
University Hospital Aachen（亚琛大学医院）；RWTH Aachen University（亚琛工业大学）；Technical University Dresden（德累斯顿工业大学）；National Center for Tumor Diseases (NCT)（国家肿瘤疾病中心 (NCT)）；University Hospital Heidelberg（海德堡大学医院）
Toward Full Autonomous Laboratory Instrumentation Control with Large Language Models
Key Laboratory of Wide Band-Gap Semiconductor Technology, School of Advanced Materials and Nanotechnology, Xidian University（西安电子科技大学先进材料与纳米技术学院宽禁带半导体技术重点实验室）
