10 papers
DiffATS: Diffusion in Aligned Tensor Space
Jinhua Lyu, Tianmin Yu, Brian Kim +3
Direct diffusion modeling of high-resolution spatiotemporal fields is computationally challenging. Parameter-efficient primitives address this by representing high-dimensional data…
Bayesian Interpolating Neural Network (B-INN): a scalable and reliable Bayesian model for large-scale physical systems
Chanwook Park, Brian Kim, Jiachen Guo +1
Neural networks and machine learning models for uncertainty quantification suffer from limited scalability and poor reliability compared to their deterministic counterparts. In ind…
A Convolutional Hierarchical Deep-learning Neural Network (C-HiDeNN) Framework for Non-linear Finite Element Analysis
Yingjian Liu, Monish Yadav Pabbala, Jiachen Guo +4
We present a framework for the Convolutional Hierarchical Deep-learning Neural Network (C-HiDeNN) tailored for nonlinear finite element analysis. Building upon the structured found…
Convolutional Hierarchical Deep Learning Neural Networks-Tensor Decomposition (C-HiDeNN-TD): a scalable surrogate modeling approach for large-scale physical systems
Jiachen Guo, Chanwook Park, Xiaoyu Xie +3
A common trend in simulation-driven engineering applications is the ever-increasing size and complexity of the problem, where classical numerical methods typically suffer from sign…
Large language model-empowered next-generation computer-aided engineering
Jiachen Guo, Chanwook Park, Dong Qian +2
Software development has entered a new era where large language models (LLMs) now serve as general-purpose reasoning engines, enabling natural language interaction and transformati…
Explainable Hierarchical Deep Learning Neural Networks (Ex-HiDeNN)
Reza T. Batley, Chanwook Park, Wing Kam Liu +1
Data-driven science and computation have advanced immensely to construct complex functional relationships using trainable parameters. However, efficiently discovering interpretable…