6 citations · 8 across the 4 of their papers we have counts for
5 papers
ChronoLLM: Customizing Language Models for Physics-Based Simulation Code Generation
Jingquan Wang, Andrew Negrut, Harry Zhang +5
This contribution is concerned with the following issue: can pretrained large language models (LLMs) be refined and customized to the point where they become virtual assistants hel…
Rethinking Agent Design: From Top-Down Workflows to Bottom-Up Skill Evolution
Jiawei Du, Jinlong Wu, Yuzheng Chen +3
Most LLM-based agent frameworks adopt a top-down philosophy: humans decompose tasks, define workflows, and assign agents to execute each step. While effective on benchmark-style ta…
ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono
Jingquan Wang, Harry Zhang, Khailanii Slaton +4
Recently, the integration of advanced simulation technologies with artificial intelligence (AI) is revolutionizing science and engineering research. ChronoLlama introduces a novel…
MBD-NODE: Physics-informed data-driven modeling and simulation of constrained multibody systems
Jingquan Wang, Shu Wang, Huzaifa Mustafa Unjhawala +2
We describe a framework that can integrate prior physical information, e.g., the presence of kinematic constraints, to support data-driven simulation in multi-body dynamics. Unlike…
Using a Bayesian-Inference Approach to Calibrating Models for Simulation in Robotics
Huzaifa Mustafa Unjhawala, Ruochun Zhang, Wei Hu +3
In robotics, simulation has the potential to reduce design time and costs, and lead to a more robust engineered solution and a safer development process. However, the use of simula…