Physics-guided, physics-informed, and physics-encoded neural networks in scientific computing
Recent breakthroughs in computing power have made it feasible to use machine learning
and deep learning to advance scientific computing in many fields, including fluid mechanics …
and deep learning to advance scientific computing in many fields, including fluid mechanics …
Physics-guided, physics-informed, and physics-encoded neural networks and operators in scientific computing: Fluid and solid mechanics
SA Faroughi, NM Pawar… - Journal of …, 2024 - asmedigitalcollection.asme.org
Advancements in computing power have recently made it possible to utilize machine
learning and deep learning to push scientific computing forward in a range of disciplines …
learning and deep learning to push scientific computing forward in a range of disciplines …
[HTML][HTML] A-PINN: Auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations
Physics informed neural networks (PINNs) are a novel deep learning paradigm primed for
solving forward and inverse problems of nonlinear partial differential equations (PDEs). By …
solving forward and inverse problems of nonlinear partial differential equations (PDEs). By …
Novel physics-informed artificial neural network architectures for system and input identification of structural dynamics PDEs
S Moradi, B Duran, S Eftekhar Azam, M Mofid - Buildings, 2023 - mdpi.com
Herein, two novel Physics Informed Neural Network (PINN) architectures are proposed for
output-only system identification and input estimation of dynamic systems. Using merely …
output-only system identification and input estimation of dynamic systems. Using merely …
A computational framework for nanotrusses: Input convex neural networks approach
The present research aims to provide a practical numerical tool for the mechanical analysis
of nanoscale trusses with similar accuracy to molecular dynamics (MD). As a first step, MD …
of nanoscale trusses with similar accuracy to molecular dynamics (MD). As a first step, MD …
Discussing the Spectra of Physics-Enhanced Machine Learning via a Survey on Structural Mechanics Applications
The intersection of physics and machine learning has given rise to a paradigm that we refer
to here as physics-enhanced machine learning (PEML), aiming to improve the capabilities …
to here as physics-enhanced machine learning (PEML), aiming to improve the capabilities …
Physics-informed neural network for turbulent flow reconstruction in composite porous-fluid systems
This study explores the implementation of physics-informed neural networks (PINNs) to
analyze turbulent flow in composite porous-fluid systems. These systems are composed of a …
analyze turbulent flow in composite porous-fluid systems. These systems are composed of a …
Physics-informed neural networks for one-step-ahead prediction of dynamical systems
M Haywood-Alexander… - …, 2023 - dpi-proceedings.com.destechpub …
During online implementation of vibration-based structural health monitoring (SHM)
strategies, forward prediction of the system state may allow for improved detection speed …
strategies, forward prediction of the system state may allow for improved detection speed …
Quantification of elastic incompatibilities at triple junctions via physics-based surrogate models
Stresses resulting from elastic incompatibilities at grain boundaries have long been known
to drive the premature failure and loss of desirable macroscopic properties in polycrystalline …
to drive the premature failure and loss of desirable macroscopic properties in polycrystalline …
Recovering the Forcing Function in Systems with One Degree of Freedom Using ANN and Physics Information
In engineering design, oftentimes a system's dynamic response is known or can be
measured, but the source generating these responses is not known. The mathematical …
measured, but the source generating these responses is not known. The mathematical …