14 citations · 22 across the 15 of their papers we have counts for
6 papers · 1 filter
Geometry-Preserving Reduced-Order Modeling via Immersed Tensor Decomposition (ITD)
Lei Zhang, Jiachen Guo, Guowei He +2
Body-fitted finite-element methods deliver high-order accuracy but hinge on a clean, watertight, conforming mesh, a requirement that breaks down for the geometrically imperfect CAD…
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…
MultiLevel Variational MultiScale (ML-VMS) framework for large-scale simulation
Lei Zhang, Jiachen Guo, Shaoqiang Tang +2
In this paper, we propose the MultiLevel Variational MultiScale (ML-VMS) method, a novel approach that seamlessly integrates a multilevel mesh strategy into the Variational Multisc…
Extended tensor decomposition model reduction methods: training, prediction, and design under uncertainty
Ye Lu, Satyajit Mojumder, Jiachen Guo +2
This paper introduces an extended tensor decomposition (XTD) method for model reduction. The proposed method is based on a sparse non-separated enrichment to the conventional tenso…
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…