197 citations · 402 across the 13 of their papers we have counts for
15 papers
Plasolver: Physics-Informed Neural Operators for Elastoplasticity
Yizheng Wang, Mohammad Sadegh Eshaghi, Huadong Zhang +3
Elastoplastic analysis is computationally demanding because its nonlinear, path-dependent constitutive behavior requires incremental loading and repeated iterative solutions. To ad…
Replay-Based Continual Learning for Physics-Informed Neural Operators
Yizheng Wang, Mohammad Sadegh Eshaghi, Xiaoying Zhuang +2
Neural operators generally demonstrate strong predictive performance on in-distribution (ID) problems. However, a critical limitation of existing methods is their significant perfo…
Deep Energy Method with Large Language Model assistance: an open-source Streamlit-based platform for solving variational PDEs
Yizheng Wang, Cosmin Anitescu, Mohammad Sadegh Eshaghi +3
Physics-informed neural networks (PINNs) in energy form, also known as the deep energy method (DEM), offer advantages over strong-form PINNs such as lower-order derivatives and few…
Pretrain Finite Element Method: A Pretraining and Warm-start Framework for PDEs via Physics-Informed Neural Operators
Yizheng Wang, Zhongkai Hao, Mohammad Sadegh Eshaghi +4
We propose a Pretrained Finite Element Method (PFEM),a physics driven framework that bridges the efficiency of neural operator learning with the accuracy and robustness of classica…
Physics-informed Machine Learning for Static Friction Modeling in Robotic Manipulators Based on Kolmogorov-Arnold Networks
Yizheng Wang, Timon Rabczuk, Yinghua Liu
Friction modeling plays a crucial role in achieving high-precision motion control in robotic operating systems. Traditional static friction models (such as the Stribeck model) are…
Towards Unified AI-Driven Fracture Mechanics: The Extended Deep Energy Method (XDEM)
Yizheng Wang, Yuzhou Lin, Somdatta Goswami +8
Physics-Informed Neural Networks (PINNs) have recently emerged as powerful tools for solving partial differential equations (PDEs), with the Deep Energy Method (DEM) proving especi…
Transfer Learning in Physics-Informed Neural Networks: Full Fine-Tuning, Lightweight Fine-Tuning, and Low-Rank Adaptation
Yizheng Wang, Jinshuai Bai, Mohammad Sadegh Eshaghi +4
AI for PDEs has garnered significant attention, particularly Physics-Informed Neural Networks (PINNs). However, PINNs are typically limited to solving specific problems, and any ch…
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…
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…
DCEM: A deep complementary energy method for solid mechanics
Yizheng Wang, Jia Sun, Timon Rabczuk +1
In recent years, the rapid advancement of deep learning has significantly impacted various fields, particularly in solving partial differential equations (PDEs) in the realm of sol…
BINN: A deep learning approach for computational mechanics problems based on boundary integral equations
Jia Sun, Yinghua Liu, Yizheng Wang +2
We proposed the boundary-integral type neural networks (BINN) for the boundary value problems in computational mechanics. The boundary integral equations are employed to transfer a…
PETS-SWINF: A regression method that considers images with metadata based Neural Network for pawpularity prediction on 2021 Kaggle Competition "PetFinder.my"
Yizheng Wang, Yinghua Liu
Millions of stray animals suffer on the streets or are euthanized in shelters every day around the world. In order to better adopt stray animals, scoring the pawpularity (cuteness)…
CENN: Conservative energy method based on neural networks with subdomains for solving variational problems involving heterogeneous and complex geometries
Yizheng Wang, Jia Sun, Wei Li +2
We propose a conservative energy method based on neural networks with subdomains for solving variational problems (CENN), where the admissible function satisfying the essential bou…