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
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
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 learning of governing equations from scarce data
Sign Up to like & getrecommendations! Published in 2021 at "Nature Communications"
DOI: 10.1038/s41467-021-26434-1
Abstract: Harnessing data to discover the underlying governing laws or equations that describe the behavior of complex physical systems can significantly advance our modeling, simulation and understanding of such systems in various science and engineering disciplines.… read more here.
Keywords: informed learning; learning governing; physics informed; physics ... 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
Parsimonious Physics-Informed Random Projection Neural Networks for Initial-Value Problems of ODEs and index-1 DAEs
Sign Up to like & getrecommendations! Published in 2022 at "Chaos"
DOI: 10.1063/5.0135903
Abstract: We present a numerical method based on random projections with Gaussian kernels and physics-informed neural networks for the numerical solution of initial value problems (IVPs) of nonlinear stiff ordinary differential equations (ODEs) and index-1 differential… read more here.
Keywords: index; neural networks; physics; initial value ... 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
Deep-pre-trained-FWI: where supervised learning meets the physics-informed neural networks
Sign Up to like & getrecommendations! Published in 2023 at "Geophysical Journal International"
DOI: 10.1093/gji/ggad215
Abstract: Full-Waveform Inversion (FWI) is the current standard method to determine final and detailed model parameters to be used in the seismic imaging process. However, FWI is an ill-posed problem that easily achieves a local minimum,… read more here.
Keywords: physics; supervised learning; model; physics informed ... See more keywords
Commentary on ‘Physics-informed deep learning parameterization of ocean vertical mixing improves climate simulations’ by Zhu et al.
Sign Up to like & getrecommendations! Published in 2022 at "National Science Review"
DOI: 10.1093/nsr/nwac092
Abstract: Climate models constitute an essential tool to understand our planet, as they implement the laws of physics describing the ocean, land and atmosphere dynamics. However, resolving processes at fine resolutions constitute an important computational bottleneck.… read more here.
Keywords: parameterization; physics; commentary physics; learning ... See more keywords
Physics-informed machine learning of the Lagrangian dynamics of velocity gradient tensor
Sign Up to like & getrecommendations! Published in 2021 at "Physical Review Fluids"
DOI: 10.1103/physrevfluids.6.094607
Abstract: Reduced models describing the Lagrangian dynamics of the Velocity Gradient Tensor (VGT) in Homogeneous Isotropic Turbulence (HIT) are developed under the Physics-Informed Machine Learning (PIML) framework. We consider VGT at both Kolmogorov scale and coarse-grained… read more here.
Keywords: gradient tensor; lagrangian dynamics; physics informed; physics ... See more keywords
DSFA-PINN: Deep Spectral Feature Aggregation Physics Informed Neural Network
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3153056
Abstract: Solving parametric partial differential equations using artificial intelligence is taking the pace. It is primarily because conventional numerical solvers are computationally expensive and require significant time to converge a solution. However, physics informed deep learning… read more here.
Keywords: neural network; spectral feature; physics; physics informed ... See more keywords