Research
Connecting large language models with mechanical engineering
to build autonomous design systems.
Research Interests
Agentic AI for Engineering Design
LLM-based multi-agent systems that reason about, decompose, and autonomously solve multidisciplinary engineering design problems.
3D Surrogate Modeling for Design Optimization
Data-driven surrogates built on geometry-aware neural networks (point clouds, meshes, graphs) that replace costly simulations and enable scalable design optimization.
Proposed Frameworks
LLM-Guided Adaptive Penalty
An LLM adaptively tunes penalty parameters inside Analytical Target Cascading for constrained design.
AutoATC
Automatically decomposes complex multidisciplinary design problems using LLM-based agents.
DialogCAD
A dialogue-driven CAD generation framework powered by a multi-agent system over the Model Context Protocol.
Point-DeepONet
Predicts nonlinear physical fields on arbitrary 3D shapes under varying load conditions.
Physics-Constrained GNN
Spatio-temporal prediction of drop impact on OLED display panels with physics constraints.
BMO-GNN
Bayesian mesh optimization that boosts engineering performance prediction with graph neural networks.