Publications (10)
Scalable Transformer for PDE Surrogate Modeling
Zijie Li, Dule Shu, Amir Barati Farimani
Transformer has shown state-of-the-art performance on various applications and has recently emerged as a promising tool for surrogate modeling of partial differential equations (PD…
Latent Neural PDE Solver: a reduced-order modelling framework for partial differential equations
Zijie Li, Saurabh Patil, Francis Ogoke +5
Neural networks have shown promising potential in accelerating the numerical simulation of systems governed by partial differential equations (PDEs). Different from many existing n…
Inpainting Computational Fluid Dynamics with Deep Learning
Dule Shu, Wilson Zhen, Zijie Li +1
Fluid data completion is a research problem with high potential benefit for both experimental and computational fluid dynamics. An effective fluid data completion method reduces th…
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…
A Physics-informed Diffusion Model for High-fidelity Flow Field Reconstruction
Dule Shu, Zijie Li, Amir Barati Farimani
Machine learning models are gaining increasing popularity in the domain of fluid dynamics for their potential to accelerate the production of high-fidelity computational fluid dyna…
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…
Zero-Shot Uncertainty Quantification using Diffusion Probabilistic Models
Dule Shu, Amir Barati Farimani
The success of diffusion probabilistic models in generative tasks, such as text-to-image generation, has motivated the exploration of their application to regression problems commo…
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…
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…
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…