197 citations · 229 across the 22 of their papers we have counts for
6 papers · 1 filter
Variational Physics-informed Neural Operator (VINO) for Solving Partial Differential Equations
Mohammad Sadegh Eshaghi, Cosmin Anitescu, Manish Thombre +3
Solving partial differential equations (PDEs) is a required step in the simulation of natural and engineering systems. The associated computational costs significantly increase whe…
Energy-based physics-informed neural network for frictionless contact problems under large deformation
Jinshuai Bai, Zhongya Lin, Yizheng Wang +5
Numerical methods for contact mechanics are of great importance in engineering applications, enabling the prediction and analysis of complex surface interactions under various cond…
Artificial intelligence for partial differential equations in computational mechanics: A review
Yizheng Wang, Jinshuai Bai, Zhongya Lin +9
In recent years, Artificial intelligence (AI) has become ubiquitous, empowering various fields, especially integrating artificial intelligence and traditional science (AI for Scien…
Applications of Scientific Machine Learning for the Analysis of Functionally Graded Porous Beams
Mohammad Sadegh Eshaghi, Mostafa Bamdad, Cosmin Anitescu +3
This study investigates different Scientific Machine Learning (SciML) approaches for the analysis of functionally graded (FG) porous beams and compares them under a new framework.…
Kolmogorov Arnold Informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov Arnold Networks
Yizheng Wang, Jia Sun, Jinshuai Bai +5
AI for partial differential equations (PDEs) has garnered significant attention, particularly with the emergence of Physics-informed neural networks (PINNs). The recent advent of K…
A Pretraining-Finetuning Computational Framework for Material Homogenization
Yizheng Wang, Xiang Li, Ziming Yan +5
Homogenization is a fundamental tool for studying multiscale physical phenomena. Traditional numerical homogenization methods, heavily reliant on finite element analysis, demand si…