105 citations · 120 across the 8 of their papers we have counts for
9 papers
Interpolating Neural Operator (INO): A Data-Free and Efficient Approach for Learning PDE Solution Operators
Jiachen Guo, Ye Lu, Naichen Shi +2
Neural operators have become a popular approach to approximate the solution operators of parametric partial differential equations (PDEs). However, existing neural operators either…
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…
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…
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…
Data-driven discovery of dimensionless numbers and scaling laws from experimental measurements
Xiaoyu Xie, Wing Kam Liu, Zhengtao Gan
Dimensionless numbers and scaling laws provide elegant insights into the characteristic properties of physical systems. Classical dimensional analysis and similitude theory fail to…