Assessing Large Language Models in Mechanical Engineering Education: A Study on Mechanics-Focused Conceptual Understanding

no code implementations • 13 Jan 2024 • Jie Tian, Jixin Hou, Zihao Wu, Peng Shu, Zhengliang Liu, Yujie Xiang, Beikang Gu, Nicholas Filla, Yiwei Li, Ning Liu, Xianyan Chen, Keke Tang, Tianming Liu, Xianqiao Wang

This study is a pioneering endeavor to investigate the capabilities of Large Language Models (LLMs) in addressing conceptual questions within the domain of mechanical engineering with a focus on mechanics.

GPT-3.5 GPT-4 +2

Exploring hyperelastic material model discovery for human brain cortex: multivariate analysis vs. artificial neural network approaches

no code implementations • 16 Oct 2023 • Jixin Hou, Nicholas Filla, Xianyan Chen, Mir Jalil Razavi, Tianming Liu, Xianqiao Wang

This study validates the applicability and accuracy of artificial neural network to automatically discover constitutive material models with proper regularization as well as the benefits in model simplification without compromising accuracy for traditional multivariable regression.

Model Discovery regression

PharmacyGPT: The AI Pharmacist

no code implementations • 19 Jul 2023 • Zhengliang Liu, Zihao Wu, Mengxuan Hu, Bokai Zhao, Lin Zhao, Tianyi Zhang, Haixing Dai, Xianyan Chen, Ye Shen, Sheng Li, Brian Murray, Tianming Liu, Andrea Sikora

In this study, we introduce PharmacyGPT, a novel framework to assess the capabilities of large language models (LLMs) such as ChatGPT and GPT-4 in emulating the role of clinical pharmacists.

GPT-4