Enhanced physics‐informed neural networks for hyperelasticity
Sign Up to like & getrecommendations! Published in 2022 at "International Journal for Numerical Methods in Engineering"
DOI: 10.1002/nme.7176
Abstract: Physics‐informed neural networks have gained growing interest. Specifically, they are used to solve partial differential equations governing several physical phenomena. However, physics‐informed neural network models suffer from several issues and can fail to provide accurate… read more here.
Keywords: informed neural; neural networks; physics; loss ... See more keywords
Physics-informed neural networks and functional interpolation for stiff chemical kinetics.
Sign Up to like & getrecommendations! Published in 2022 at "Chaos"
DOI: 10.1063/5.0086649
Abstract: This work presents a recently developed approach based on physics-informed neural networks (PINNs) for the solution of initial value problems (IVPs), focusing on stiff chemical kinetic problems with governing equations of stiff ordinary differential equations… read more here.
Keywords: informed neural; neural networks; time; physics ... See more keywords
Physics-informed Neural Network method for predicting soliton dynamics supported by complex PT-symmetric potentials
Sign Up to like & getrecommendations! Published in 2023 at "Chinese Physics Letters"
DOI: 10.1088/0256-307x/40/6/070501
Abstract: We examine the deep learning technique referred to as the physics-informed neural network method for approximating non-linear Schrödinger equation under considered parity time symmetric potentials and obtaining multifarious soliton solutions. For the first time, neural… read more here.
Keywords: neural network; network; informed neural; physics ... See more keywords
Single Reference Frequency Loss for Multifrequency Wavefield Representation Using Physics-Informed Neural Networks
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2022.3176867
Abstract: Physics-informed neural networks (PINNs) can offer approximate multidimensional functional solutions to the Helmholtz equation that is flexible, requires low memory, and has no limitations on the shape of the solution space. However, the neural network… read more here.
Keywords: informed neural; function; physics; frequency ... See more keywords
Applications of Physics-Informed Neural Network for Optical Fiber Communications
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Communications Magazine"
DOI: 10.1109/mcom.001.2100961
Abstract: Due to the capability of the physics-informed neural network (PINN) to solve complex partial differential equations automatically, it has revolutionized the field of scientific computing. This article studies the applicability of PINN in optical fiber… read more here.
Keywords: neural network; informed neural; physics; fiber ... See more keywords
Physics-Informed Neural Networks for Solving Parametric Magnetostatic Problems
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Energy Conversion"
DOI: 10.1109/tec.2022.3180295
Abstract: The objective of this paper is to investigate the ability of physics-informed neural networks to learn the magnetic field response as a function of design parameters in the context of a two-dimensional (2-D) magnetostatic problem.… read more here.
Keywords: informed neural; neural networks; parametric magnetostatic; physics ... See more keywords
Upwind, No More: Flexible Traveltime Solutions Using Physics-Informed Neural Networks
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2022.3218754
Abstract: The eikonal equation plays an important role across multidisciplinary branches of science and engineering. In geophysics, the eikonal equation and its characteristics are used in addressing two fundamental questions pertaining to seismic waves: what paths… read more here.
Keywords: informed neural; physics; eikonal equation; upwind flexible ... See more keywords
A High-Efficient Hybrid Physics-Informed Neural Networks Based on Convolutional Neural Network.
Sign Up to like & getrecommendations! Published in 2021 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2021.3070878
Abstract: In this article, we develop a hybrid physics-informed neural network (hybrid PINN) for partial differential equations (PDEs). We borrow the idea from the convolutional neural network (CNN) and finite volume methods. Unlike the physics-informed neural… read more here.
Keywords: neural network; physics; hybrid physics; informed neural ... See more keywords
Elasticity Imaging Using Physics-informed Neural Networks: Spatial Discovery of Elastic Modulus and Poisson's Ratio.
Sign Up to like & getrecommendations! Published in 2022 at "Acta biomaterialia"
DOI: 10.2139/ssrn.4203110
Abstract: Elasticity imaging is a technique that discovers the spatial distribution of mechanical properties of tissue using deformation and force measurements under various loading conditions. Given the complexity of this reconstruction, most existing methods approximate only… read more here.
Keywords: elasticity imaging; informed neural; neural networks; physics ... See more keywords
A Preliminary Study on the Resolution of Electro-Thermal Multi-Physics Coupling Problem Using Physics-Informed Neural Network (PINN)
Sign Up to like & getrecommendations! Published in 2022 at "Algorithms"
DOI: 10.3390/a15020053
Abstract: The problem of electro-thermal coupling is widely present in the integrated circuit (IC). The accuracy and efficiency of traditional solution methods, such as the finite element method (FEM), are tightly related to the quality and… read more here.
Keywords: problem; neural network; informed neural; physics ... See more keywords
Protein Design Using Physics Informed Neural Networks
Sign Up to like & getrecommendations! Published in 2023 at "Biomolecules"
DOI: 10.3390/biom13030457
Abstract: The inverse protein folding problem, also known as protein sequence design, seeks to predict an amino acid sequence that folds into a specific structure and performs a specific function. Recent advancements in machine learning techniques… read more here.
Keywords: informed neural; neural networks; protein design; physics ... See more keywords