Physics-Informed Deep-Learning For Elasticity: Forward, Inverse, and Mixed Problems.
Sign Up to like & getrecommendations! Published in 2023 at "Advanced science"
DOI: 10.1002/advs.202300439
Abstract: Elastography is a medical imaging technique used to measure the elasticity of tissues by comparing ultrasound signals before and after a light compression. The lateral resolution of ultrasound is much inferior to the axial resolution.… read more here.
Keywords: physics; elasticity; material; deep learning ... See more keywords
Physics‐informed learning of chemical reactor systems using decoupling–coupling training framework
Sign Up to like & getrecommendations! Published in 2024 at "AIChE Journal"
DOI: 10.1002/aic.18436
Abstract: It is known that physics‐informed learning become a new learning philosophy that has been applied in many scientific domains. However, this approach often struggles to achieve optimal performance in addressing the issue of multiphysics coupling.… read more here.
Keywords: physics informed; coupling training; reactor; training ... See more keywords
Physics‐informed reinforcement learning for optimal control of nonlinear systems
Sign Up to like & getrecommendations! Published in 2024 at "AIChE Journal"
DOI: 10.1002/aic.18542
Abstract: This article proposes a model‐free framework to solve the optimal control problem with an infinite‐horizon performance function for nonlinear systems with input constraints. Specifically, two Physics‐Informed Neural Networks (PINNs) that incorporate the knowledge of the… read more here.
Keywords: physics informed; nonlinear systems; control; optimal control ... See more keywords
Physics‐informed neural networks for biopharmaceutical cultivation processes: Consideration of varying process parameter settings
Sign Up to like & getrecommendations! Published in 2024 at "Biotechnology and Bioengineering"
DOI: 10.1002/bit.28851
Abstract: We present a new modeling approach for the study and prediction of important process outcomes of biotechnological cultivation processes under the influence of process parameter variations. Our model is based on physics‐informed neural networks (PINNs)… read more here.
Keywords: process; physics informed; cultivation processes; process parameter ... See more keywords
Accurate and Rapid Ranking of Protein–Ligand Binding Affinities Using Density Matrix Fragmentation and Physics‐Informed Machine Learning Dispersion Potentials
Sign Up to like & getrecommendations! Published in 2025 at "Chemphyschem"
DOI: 10.1002/cphc.202500094
Abstract: The generalized many‐body expansion for building density matrices (GMBE‐DM), truncated at the one‐body level and combined with a purification scheme, is applied to rank protein–ligand binding affinities across two cyclin‐dependent kinase 2 (CDK2) datasets and… read more here.
Keywords: physics informed; protein ligand; dispersion; ligand binding ... See more keywords
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
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
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
A physics-informed deep learning framework for dynamic susceptibility contrast perfusion MRI.
Sign Up to like & getrecommendations! Published in 2024 at "Medical physics"
DOI: 10.1002/mp.17415
Abstract: BACKGROUND Perfusion magnetic resonance imaging (MRI)s plays a central role in the diagnosis and monitoring of neurovascular or neurooncological disease. However, conventional processing techniques are limited in their ability to capture relevant characteristics of the… read more here.
Keywords: physics informed; perfusion mri; contrast; perfusion ... See more keywords
Improved unsupervised physics‐informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patients
Sign Up to like & getrecommendations! Published in 2021 at "Magnetic Resonance in Medicine"
DOI: 10.1002/mrm.28852
Abstract: Earlier work showed that IVIM‐NETorig, an unsupervised physics‐informed deep neural network, was faster and more accurate than other state‐of‐the‐art intravoxel‐incoherent motion (IVIM) fitting approaches to diffusion‐weighted imaging (DWI). This study presents a substantially improved version,… read more here.
Keywords: physics informed; intravoxel incoherent; physics; incoherent motion ... See more keywords