Residual-based attention in physics-informed neural networks
SJ Anagnostopoulos, JD Toscano… - Computer Methods in …, 2024 - Elsevier
Driven by the need for more efficient and seamless integration of physical models and data,
physics-informed neural networks (PINNs) have seen a surge of interest in recent years …
physics-informed neural networks (PINNs) have seen a surge of interest in recent years …
Optimal control of PDEs using physics-informed neural networks
Physics-informed neural networks (PINNs) have recently become a popular method for
solving forward and inverse problems governed by partial differential equations (PDEs). By …
solving forward and inverse problems governed by partial differential equations (PDEs). By …
Machine learning and domain decomposition methods-a survey
Hybrid algorithms, which combine black-box machine learning methods with experience
from traditional numerical methods and domain expertise from diverse application areas, are …
from traditional numerical methods and domain expertise from diverse application areas, are …
Estimating density, velocity, and pressure fields in supersonic flows using physics-informed BOS
We report a new workflow for background-oriented schlieren (BOS), termed “physics-
informed BOS,” to extract density, velocity, and pressure fields from a pair of reference and …
informed BOS,” to extract density, velocity, and pressure fields from a pair of reference and …
[HTML][HTML] Variational temporal convolutional networks for I-FENN thermoelasticity
DW Abueidda, ME Mobasher - Computer Methods in Applied Mechanics …, 2024 - Elsevier
Abstract Machine learning (ML) has been used to solve multiphysics problems like
thermoelasticity through multi-layer perceptron (MLP) networks. However, MLPs have high …
thermoelasticity through multi-layer perceptron (MLP) networks. However, MLPs have high …
Investigating and mitigating failure modes in physics-informed neural networks (pinns)
S Basir - arXiv preprint arXiv:2209.09988, 2022 - arxiv.org
This paper explores the difficulties in solving partial differential equations (PDEs) using
physics-informed neural networks (PINNs). PINNs use physics as a regularization term in …
physics-informed neural networks (PINNs). PINNs use physics as a regularization term in …
Enhanced physics-informed neural networks with augmented Lagrangian relaxation method (AL-PINNs)
Abstract Physics-Informed Neural Networks (PINNs) have become a prominent application
of deep learning in scientific computation, as they are powerful approximators of solutions to …
of deep learning in scientific computation, as they are powerful approximators of solutions to …
Critical investigation of failure modes in physics-informed neural networks
View Video Presentation: https://doi. org/10.2514/6.2022-2353. vid Several recent works in
scientific machine learning have revived interest in the application of neural networks to …
scientific machine learning have revived interest in the application of neural networks to …
Self-adaptive weights based on balanced residual decay rate for physics-informed neural networks and deep operator networks
Physics-informed deep learning has emerged as a promising alternative for solving partial
differential equations. However, for complex problems, training these networks can still be …
differential equations. However, for complex problems, training these networks can still be …
Machine learning-based soil–structure interaction analysis of laterally loaded piles through physics-informed neural networks
This research adopts emerging machine learning techniques to tackle the soil–structure
interaction analysis problems of laterally loaded piles through physics-informed neural …
interaction analysis problems of laterally loaded piles through physics-informed neural …