120 citations · 233 across the 8 of their papers we have counts for
8 papers
A Tutorial on Dimensionless Learning: Geometric Interpretation and the Effect of Noise
Zhengtao Jake Gan, Xiaoyu Xie
Dimensionless learning is a data-driven framework for discovering dimensionless numbers and scaling laws from experimental measurements. This tutorial introduces the method, explai…
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…
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…
Smooth and Sparse Latent Dynamics in Operator Learning with Jerk Regularization
Xiaoyu Xie, Saviz Mowlavi, Mouhacine Benosman
Spatiotemporal modeling is critical for understanding complex systems across various scientific and engineering disciplines, but governing equations are often not fully known or co…
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…
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…
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…