5 papers
LAMP: Data-Efficient Linear Affine Weight-Space Models for Parameter-Controlled 3D Shape Generation and Extrapolation
Ghadi Nehme, Yanxia Zhang, Dule Shu +2
Generating high-fidelity 3D geometries under explicit parameter constraints is central to engineering design, yet current methods often require large datasets and fail to provide r…
CarCrashNet: A Large-Scale Dataset and Hierarchical Neural Solver for Data-Driven Structural Crash Simulation
Mohamed Elrefaie, Dule Shu, Matthew Klenk +1
Crash simulation is a cornerstone of modern vehicle development because it reduces the need for costly physical prototypes, accelerates safety-driven design iteration, and increasi…
Learning From Design Procedure To Generate CAD Programs for Data Augmentation
Yan-Ying Chen, Dule Shu, Matthew Hong +3
Large Language Models (LLMs) have demonstrated impressive capabilities in a wide range of code generation tasks. However, generating code for certain domains remains challenging. O…
PLLM: Pseudo-Labeling Large Language Models for CAD Program Synthesis
Yuanbo Li, Dule Shu, Yanying Chen +2
Recovering Computer-Aided Design (CAD) programs from 3D geometries is a widely studied problem. Recent advances in large language models (LLMs) have enabled progress in CAD program…
CarBench: A Comprehensive Benchmark for Neural Surrogates on High-Fidelity 3D Car Aerodynamics
Mohamed Elrefaie, Dule Shu, Matt Klenk +1
Benchmarking has been the cornerstone of progress in computer vision, natural language processing, and the broader deep learning domain, driving algorithmic innovation through stan…