Solutions to Two‐ and Three‐Dimensional Incompressible Flow Fields Leveraging a Physics‐Informed Deep Learning Framework and Kolmogorov–Arnold Networks
Sign Up to like & getrecommendations! Published in 2025 at "International Journal for Numerical Methods in Fluids"
DOI: 10.1002/fld.5374
Abstract: Physics‐informed neural network (PINN) has become a potential technology for fluid dynamics simulations, but traditional PINN has low accuracy in simulating incompressible flows, and these problems can lead to PINN not converging. This paper proposes… read more here.
Keywords: physics informed; pinn; network; three dimensional ... See more keywords
Chien-physics-informed neural networks for solving singularly perturbed boundary-layer problems
Sign Up to like & getrecommendations! Published in 2024 at "Applied Mathematics and Mechanics"
DOI: 10.1007/s10483-024-3149-8
Abstract: A physics-informed neural network (PINN) is a powerful tool for solving differential equations in solid and fluid mechanics. However, it suffers from singularly perturbed boundary-layer problems in which there exist sharp changes caused by a… read more here.
Keywords: singularly perturbed; physics informed; pinn; informed neural ... See more keywords
Deep learning based on PINN for solving 2 DOF vortex induced vibration of cylinder
Sign Up to like & getrecommendations! Published in 2021 at "Ocean Engineering"
DOI: 10.1016/j.oceaneng.2021.109932
Abstract: Abstract Vortex-induced vibration (VIV) exists widely in natural and industrial fields. The main approaches for solving VIV problems are numerical simulations and experimental methods. However, experiment methods are difficult to obtain the whole flow field… read more here.
Keywords: vortex induced; pinn solving; induced vibration; pinn ... See more keywords
Strategies for multi-case physics-informed neural networks for tube flows: a study using 2D flow scenarios
Sign Up to like & getrecommendations! Published in 2024 at "Scientific Reports"
DOI: 10.1038/s41598-024-62117-9
Abstract: Fluid dynamics computations for tube-like geometries are crucial in biomedical evaluations of vascular and airways fluid dynamics. Physics-Informed Neural Networks (PINNs) have emerged as a promising alternative to traditional computational fluid dynamics (CFD) methods. However,… read more here.
Keywords: pinn; multi case; case; network ... See more keywords
Physics-informed neural networks for transonic flow around a cylinder with high Reynolds number
Sign Up to like & getrecommendations! Published in 2024 at "Physics of Fluids"
DOI: 10.1063/5.0200384
Abstract: The physics-informed neural network (PINN) method is extended to learn and predict compressible steady-state aerodynamic flows with a high Reynolds number. To better learn the thin boundary layer, the sampling distance function and hard boundary… read more here.
Keywords: physics informed; high reynolds; pinn; informed neural ... See more keywords
A physics-informed deep learning description of Knudsen layer reactivity reduction
Sign Up to like & getrecommendations! Published in 2024 at "Physics of Plasmas"
DOI: 10.1063/5.0207372
Abstract: A physics-informed neural network (PINN) is used to evaluate the fast ion distribution in the hot spot of an inertial confinement fusion target. The use of tailored input and output layers to the neural network… read more here.
Keywords: physics informed; knudsen layer; pinn; reduction ... See more keywords
Spatially-distributed parameter identification by physics-informed neural networks illustrated on the 2D shallow-water equations
Sign Up to like & getrecommendations! Published in 2025 at "Inverse Problems"
DOI: 10.1088/1361-6420/adb0e7
Abstract: Recent advancements in Physics-Informed Neural Networks (PINNs) offer promising opportunities for the identification of parameters of physical models based on ODEs and PDEs. This work revisits two representative PINN-based approaches for inverse problems, and applies… read more here.
Keywords: physics informed; parameter; pinn; parameter identification ... See more keywords
A novel discretized physics-informed neural network model applied to the Navier–Stokes equations
Sign Up to like & getrecommendations! Published in 2024 at "Physica Scripta"
DOI: 10.1088/1402-4896/ad5592
Abstract: The advancement of scientific machine learning (ML) techniques has led to the development of methods for approximating solutions to nonlinear partial differential equations (PDE) with increased efficiency and accuracy. Automatic differentiation has played a pivotal… read more here.
Keywords: physics informed; navier stokes; pinn; informed neural ... See more keywords
Few-Shot Data-Driven Modeling of Unified Grid Tied VSCs for Multioperation Impedance Identification Based on PINN
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Industrial Electronics"
DOI: 10.1109/tie.2024.3508059
Abstract: The multioperation impedance identification of the three-phase grid tied voltage source converters (VSCs) is essential to analyze the converter-grid interaction stability considering various operating conditions. However, the existing identification methods require a substantial amount of… read more here.
Keywords: pinn; grid tied; identification; multioperation impedance ... See more keywords
Structural seismic responses prediction using the gradient-enhanced hybrid PINN
Sign Up to like & getrecommendations! Published in 2024 at "Advances in Structural Engineering"
DOI: 10.1177/13694332241260140
Abstract: To rapidly and effectively assess the bridge seismic-resistant capability, it is essential to conduct efficient predictions of bridge seismic responses. Recently, physics informed neural network (PINN) has made great progress and utilized to solve differential… read more here.
Keywords: gradient enhanced; seismic responses; hybrid pinn; pinn ... See more keywords
Physics-Informed Neural Networks for Modal Wave Field Predictions in 3D Room Acoustics
Sign Up to like & getrecommendations! Published in 2025 at "Applied Sciences"
DOI: 10.3390/app15020939
Abstract: The generalization of Physics-Informed Neural Networks (PINNs) used to solve the inhomogeneous Helmholtz equation in a simplified three-dimensional room is investigated. PINNs are appealing since they can efficiently integrate a partial differential equation and experimental… read more here.
Keywords: acoustics; feature engineered; pinn; excitation ... See more keywords