A Physics‐Informed Neural Networks Approach for Accurate Prediction of Solitary Waves in Magnetized Plasma
Sign Up to like & getrecommendations! Published in 2025 at "Contributions to Plasma Physics"
DOI: 10.1002/ctpp.70032
Abstract: In the present work, we propose a new paradigm for the simulation of solitary waves in plasma with the help of Physics‐Informed Neural Networks (PINNs). PINNs is a type of neural network architecture aimed at… read more here.
Keywords: physics informed; solitary waves; magnetized plasma; informed neural ... See more keywords
Fs‐Pinns: Fractional Spectrally Adapted Physics‐Informed Neural Networks for Fractional Partial Differential Equations
Sign Up to like & getrecommendations! Published in 2025 at "Mathematical Methods in the Applied Sciences"
DOI: 10.1002/mma.11149
Abstract: In this paper, we present a new and efficient fractional spectrally adapted physics‐informed neural network (fs‐PINN) method for solving fractional partial differential equations (PDEs). The fs‐PINN approach overcomes the computational difficulties encountered by traditional methods… read more here.
Keywords: physics informed; fractional spectrally; informed neural; spectrally adapted ... See more keywords
A Physics‐Informed Neural Network Framework for Tumor‐Immune Interactions, Metastatic Invasion, and Haptotaxis Systems
Sign Up to like & getrecommendations! Published in 2025 at "Mathematical Methods in the Applied Sciences"
DOI: 10.1002/mma.70355
Abstract: Cancer and tumor growth are complex biological processes that can be modeled using systems of ordinary differential equations (ODEs) and partial differential equations (PDEs). These models capture the dynamics of tumor progression, interactions with the… read more here.
Keywords: physics informed; immune interactions; informed neural; tumor ... See more keywords
SPINNED: Simulation‐based physics‐informed neural network for deconvolution of dynamic susceptibility contrast MRI perfusion data
Sign Up to like & getrecommendations! Published in 2024 at "Magnetic Resonance in Medicine"
DOI: 10.1002/mrm.30095
Abstract: To propose the simulation‐based physics‐informed neural network for deconvolution of dynamic susceptibility contrast (DSC) MRI (SPINNED) as an alternative for more robust and accurate deconvolution compared to existing methods. read more here.
Keywords: physics informed; based physics; informed neural; simulation based ... See more keywords
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
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
Self-knowledge Distillation Enhanced Universal Framework for Physics-Informed Neural Networks
Sign Up to like & getrecommendations! Published in 2025 at "Nonlinear Dynamics"
DOI: 10.1007/s11071-025-10916-8
Abstract: With the advancement of machine learning and deep learning, Physics-Informed Neural Networks (PINNs) have emerged as a prominent approach for solving partial differential equation (PDE) problems. In this article, we introduce a novel distillation framework… read more here.
Keywords: physics informed; knowledge distillation; framework; informed neural ... See more keywords
New insights into experimental stratified flows obtained through physics-informed neural networks
Sign Up to like & getrecommendations! Published in 2024 at "Journal of Fluid Mechanics"
DOI: 10.1017/jfm.2024.49
Abstract: Abstract We develop a physics-informed neural network (PINN) to significantly augment state-of-the-art experimental data of stratified flows. A fully connected deep neural network is trained using time-resolved experimental data in a salt-stratified inclined duct experiment,… read more here.
Keywords: physics informed; new insights; stratified flows; informed neural ... See more keywords
Automatic network structure discovery of physics informed neural networks via knowledge distillation
Sign Up to like & getrecommendations! Published in 2025 at "Nature Communications"
DOI: 10.1038/s41467-025-64624-3
Abstract: Partial differential equations (PDEs) are fundamental for modeling complex physical processes, often exhibiting structural features such as symmetries and conservation laws. While physics-informed neural networks (PINNs) can simulate and invert PDEs, they mainly rely on… read more here.
Keywords: physics informed; informed neural; distillation; network ... See more keywords
Trainable embedding quantum physics informed neural networks for solving nonlinear PDEs
Sign Up to like & getrecommendations! Published in 2025 at "Scientific Reports"
DOI: 10.1038/s41598-025-02959-z
Abstract: This paper proposes a novel approach for solving nonlinear partial differential equations (PDEs) with a quantum computer, the trainable embedding quantum physics informed neural network (TE-QPINN). We combine quantum machine learning (QML) with physics informed… read more here.
Keywords: physics informed; solving nonlinear; informed neural; quantum ... See more keywords
Physics informed neural networks for fluid flow analysis with repetitive parameter initialization
Sign Up to like & getrecommendations! Published in 2025 at "Scientific Reports"
DOI: 10.1038/s41598-025-99354-5
Abstract: Physics-informed neural networks (PINNs) have been widely used to capture the behavior of physical systems governed by partial differential equations (PDEs), enabling the simulation of fluid dynamics across various scenarios. However, when applied to stiff… read more here.
Keywords: physics informed; informed neural; fluid flow; strategy ... See more keywords