14 citations · 19 across the 12 of their papers we have counts for
12 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…
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…
INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics
Taskin Mehereen, Sourav Saha, Intesar Jawad Jaigirdar +1
The ability to accurately model interatomic interactions in large-scale systems is fundamental to understanding a wide range of physical and chemical phenomena, from drug-protein b…
Tensor-decomposition-based A Priori Surrogate (TAPS) modeling for ultra large-scale simulations
Jiachen Guo, Gino Domel, Chanwook Park +8
A data-free, predictive scientific AI model, Tensor-decomposition-based A Priori Surrogate (TAPS), is proposed for tackling ultra large-scale engineering simulations with significa…
Interpolating Neural Network-Tensor Decomposition (INN-TD): a scalable and interpretable approach for large-scale physics-based problems
Jiachen Guo, Xiaoyu Xie, Chanwook Park +5
Deep learning has been extensively employed as a powerful function approximator for modeling physics-based problems described by partial differential equations (PDEs). Despite thei…
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…
Multi-Patch Isogeometric Convolution Hierarchical Deep-learning Neural Network
Lei Zhang, Chanwook Park, T. J. R. Hughes +1
A seamless integration of neural networks with Isogeometric Analysis (IGA) was first introduced in [1] under the name of Hierarchical Deep-learning Neural Network (HiDeNN) and has…
Interpolating neural network: A novel unification of machine learning and interpolation theory
Chanwook Park, Sourav Saha, Jiachen Guo +8
Artificial intelligence (AI) has revolutionized software development, shifting from task-specific codes (Software 1.0) to neural network-based approaches (Software 2.0). However, a…
Deep Learning Discrete Calculus (DLDC): A Family of Discrete Numerical Methods by Universal Approximation for STEM Education to Frontier Research
Sourav Saha, Chanwook Park, Stefan Knapik +3
The article proposes formulating and codifying a set of applied numerical methods, coined as Deep Learning Discrete Calculus (DLDC), that uses the knowledge from discrete numerical…