105 citations · 127 across the 16 of their papers we have counts for
18 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…
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…
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…
CM-GAI: Continuum Mechanistic Generative Artificial Intelligence Theory for Data Dynamics
Shan Tang, Ziwei Cao, Zhenling Yang +4
Generative artificial intelligence (GAI) plays a fundamental role in high-impact AI-based systems such as SORA and AlphaFold. Currently, GAI shows limited capability in the special…
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…
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…
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…
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…
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…
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…
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…
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…