Enterprise AI Analysis of "Can ChatGPT Implement Finite Element Models for Geotechnical Engineering Applications?"

Authors: Taegu Kim, Tae Sup Yun, Hyoung Suk Suh

Source: arXiv:2501.02199v1 [math.NA]

Executive Summary: AI as an Engineering Accelerator, Not a Replacement

In their insightful paper, Kim, Yun, and Suh investigate a critical question for modern engineering enterprises: can Large Language Models (LLMs) like ChatGPT autonomously handle complex, specialized programming tasks? Their research focuses on generating Finite Element (FE) models for geotechnical engineeringa field requiring deep mathematical and domain-specific knowledge. The findings provide a clear, actionable roadmap for businesses looking to leverage AI for technical acceleration.

The study concludes that while ChatGPT is not yet a replacement for human engineers, it is a powerful assistive tool whose effectiveness is dramatically influenced by the underlying technology stack. When paired with high-level, domain-specific programming libraries like FEniCS, ChatGPT can successfully generate complex code with expert human guidance. However, when tasked with low-level, from-scratch implementations in environments like MATLAB, it consistently fails.

For enterprises, the key takeaway is twofold: first, the "prompt-and-forget" approach to AI is a recipe for failure in technical domains. Second, strategic investment in the right high-level software frameworks is the single most important factor for unlocking ROI from AI-assisted development. This analysis from OwnYourAI breaks down the paper's findings into actionable strategies for integrating LLMs into your engineering workflows, highlighting how a custom AI solution is the bridge between potential and performance.

Decoding the Research: Methodology and Key Findings

The research rigorously tested ChatGPT's capabilities by presenting it with three geotechnical engineering problems, each representing a different tier of complexity. The goal was to see how well the LLM could translate theoretical requirements into functional code.

Core Finding: The Platform is Paramount

The most striking result of the study is the night-and-day difference in performance between the two programming environments tested: FEniCS (a high-level Python library for Finite Element Analysis) and MATLAB (a general-purpose numerical computing environment).

• With FEniCS (High-Level): ChatGPT was largely successful. Its high-level abstractions, which handle the complex low-level mathematics of FEA automatically, allowed the LLM to focus on implementing the problem's logic. While it still required expert-guided debugging, it consistently produced working code for even the most complex models.
• With MATLAB (Low-Level): ChatGPT failed almost completely on complex problems. It struggled to correctly implement fundamental FEA components like shape functions and global matrix assembly. This resulted in code that was either non-functional or required such extensive human intervention that the AI's contribution was minimal.

The Tech Stack Dilemma: High-Level Frameworks as an ROI Multiplier

The paper's results offer a powerful lesson for any enterprise: throwing AI at an inefficient or ill-suited tech stack will not yield results. The choice of software environment is a critical enabler for successful AI implementation.

Interactive Analysis: AI Performance vs. Task Complexity

The charts below, rebuilt from the paper's data, visualize the number of "prompt augmentations" (i.e., debugging attempts) needed for ChatGPT to produce a correct solution. The difference between FEniCS and MATLAB is stark, especially as complexity increases.

Enterprise Insight: These results demonstrate that investing in high-level, domain-specific platforms is a prerequisite for leveraging AI in technical fields. A custom solution from OwnYourAI involves not just deploying an LLM, but first analyzing and optimizing your technology stack to create an environment where AI can actually succeed.

The Human-in-the-Loop Imperative: Expertise is Non-Negotiable

A critical theme throughout the paper is that ChatGPT did not succeed in a vacuum. The researchers, acting as domain experts, had to diagnose errors and provide precise, corrective prompts. For instance, they had to identify when the LLM implemented an incorrect "weak form" of a partial differential equationa subtle but fatal error that only a subject matter expert could spot.

This highlights the future of advanced technical work: a symbiotic relationship between human experts and AI tools. The AI handles the tedious task of code generation, while the human provides strategic direction, validation, and critical thinking.

Test Your Knowledge: The Role of the Expert

This short quiz, based on the paper's findings, explores this crucial human-AI dynamic.

Enterprise Implementation Roadmap & ROI Analysis

Based on the paper's findings, we can outline a strategic roadmap for integrating LLM-assisted code generation into engineering workflows. This isn't a one-step process but a phased approach focused on building capability and maximizing ROI.

A 4-Step Roadmap for AI-Assisted Engineering

1. Foundation & Assessment: Analyze your current tech stack. Identify and migrate from low-level, manual processes to high-level, automated frameworks. This is the most critical step for enabling future AI success.
2. Pilot Program: Begin with simple, well-defined problems (like Model 1). Use these projects to train your team on effective prompt engineering and establish a baseline for performance improvement.
3. Develop a Human-AI Workflow: Formalize the process of expert oversight. Create checklists for code validation, define roles for AI "prompters" and expert "reviewers," and build a library of effective prompt templates.
4. Scale & Optimize: Gradually tackle more complex problems (like Models 2 and 3). Use the learnings from the pilot program to refine your workflows and integrate the AI assistant more deeply into your development lifecycle.

Interactive ROI Calculator: Estimate Your Efficiency Gains

While the paper focuses on technical feasibility, the business implications are clear: successful AI implementation leads to significant time and cost savings. Use this calculator to estimate the potential ROI of adopting an AI-assisted workflow in your engineering department, based on principles of accelerated development and debugging.

Conclusion: Custom AI Solutions are the Key

The research by Kim, Yun, and Suh provides a clear verdict: general-purpose LLMs like ChatGPT are not a turnkey solution for specialized enterprise problems. Their success is conditional. They require the right technological foundation and expert human partnership to deliver value.

This is precisely where OwnYourAI adds critical value. We don't just provide access to an AI; we build custom, integrated solutions. Our process mirrors the paper's path to success:

• We begin by analyzing and optimizing your technology stack, ensuring you have the high-level frameworks necessary for AI to thrive.
• We develop custom-tuned LLM assistants that understand the specific nuances of your domain.
• We design and implement robust human-in-the-loop workflows that empower your experts, turning them into highly efficient AI collaborators.