120 citations · 187 across the 12 of their papers we have counts for
13 papers
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…
Laser Powder Bed Fusion Melt Pool Dynamics for Different Geometric Variations and Powder Layer Heights: High-Fidelity Multiphysics Modeling vs 2025 NIST Experiments
Badhon Kumar, Rakibul Islam Kanak, Nishat Sultana +4
Metal Laser Powder Bed Fusion (PBF-LB/M) is a leading additive manufacturing technique in which part quality and grain morphology are highly dependent on process parameters. Numero…
Efficient GPU-computing simulation platform JAX-PF for differentiable phase field model
Fanglei Hu, Jiachen Guo, Stephen Niezgoda +2
We present JAX-PF, an open-source, GPU-accelerated, and differentiable Phase Field (PF) software package, supporting both explicit and implicit time stepping schemes. Leveraging th…
Time-marching multi-level variational multiscale tensor decomposition algorithm for heat conduction with moving heat source
Xinyi Guan, Jiayi Hu, Lei Zhang +2
In this paper, we propose a time-marching multi-level Variational Multiscale-Tensor Decomposition (VMS-TD) algorithm to solve the heat equation with a moving heat source model that…
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…
Statistical Parameterized Physics-Based Machine Learning Digital Twin Models for Laser Powder Bed Fusion Process
Yangfan Li, Satyajit Mojumder, Ye Lu +7
A digital twin (DT) is a virtual representation of physical process, products and/or systems that requires a high-fidelity computational model for continuous update through the int…
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…
JAX-FEM: A differentiable GPU-accelerated 3D finite element solver for automatic inverse design and mechanistic data science
Tianju Xue, Shuheng Liao, Zhengtao Gan +4
This paper introduces JAX-FEM, an open-source differentiable finite element method (FEM) library. Constructed on top of Google JAX, a rising machine learning library focusing on hi…
An Introduction to Kernel and Operator Learning Methods for Homogenization by Self-consistent Clustering Analysis
Owen Huang, Sourav Saha, Jiachen Guo +1
Recent advances in operator learning theory have improved our knowledge about learning maps between infinite dimensional spaces. However, for large-scale engineering problems such…
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…
Stochastic additive manufacturing simulation: from experiment to surface roughness and porosity prediction
Yangfan Li, Ye Lu, Abdullah Al Amin +1
Deterministic computational modeling of laser powder bed fusion (LPBF) process fails to capture irregularities and roughness of the scan track, unless expensive powder-scale analys…
Mechanistic Data Science for Modeling and Design of Aerospace Composite Materials
Satyajit Mojumder, Lei Tao, Ying Li +1
Polymer matrix composites exhibit remarkable lightweight and high strength properties that make them attractive for aerospace applications. Constituents' materials such as advanced…
HiDeNN-PGD: reduced-order hierarchical deep learning neural networks
Lei Zhang, Ye Lu, Shaoqiang Tang +1
This paper presents a proper generalized decomposition (PGD) based reduced-order model of hierarchical deep-learning neural networks (HiDeNN). The proposed HiDeNN-PGD method keeps…