3 papers
math.NA2025
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…
cs.CE2025
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…
cs.CE2025
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…