Results 131 to 140 of about 8,116 (238)

Learning Adaptive Filtering in the Fourier Domain for Fringe Pattern Analysis: A New Reconstruction Paradigm in Digital Holographic Interferometry

Nanophotonics, Volume 15, Issue 20, 27 October 2026.
The object beam and the reference beam interfere at the camera sensor surface and multi‐angle tomographic recording based on off‐axis digital holography. ABSTRACT Single‐frame fringe pattern analysis in digital holographic interferometry (DHI) is essential for precision phase measurement under dynamic conditions. Recent advances in deep learning within
Youxing Li, Lingzhi Meng, Zhiyuan Xu, Hongwei Li, Kai Zhang, Yaoqing Xie, Libo Yuan   +6 more
wiley   +1 more source

linuswalter/PINN-for-Subsurface-Flow

This software computes the diffusion of fluid pressure p(x,t) in a 1D domain based on the concept of Physics Informed Neural Networks (PINN) for a heterogeneous modeling domain.GNU General Public License 2 or later (GPL-2.0)Field-data assimilation to ...
Walter, Linus
core   +1 more source

A Gradient-Variance Weighting Physics-Informed Neural Network for Solving Integer and Fractional Partial Differential Equations

Applied Sciences
Physics-Informed Neural Networks (PINNs) have emerged as a promising paradigm for solving partial differential equations (PDEs) by embedding physical laws into the learning process.
Liang Zhang, Quansheng Liu, Ruigang Zhang, Liqing Yue, Zhaodong Ding   +4 more
doaj   +1 more source

Adaptive Interface-PINNs (AdaI-PINNs): An Efficient Physics-Informed Neural Networks Framework for Interface Problems

Communications in Computational Physics
17 pages, 8 figures, 6 ...
Sumanta Roy, Chandrasekhar Annavarapu, Pratanu Roy, Antareep Kumar Sarma   +3 more
openaire   +2 more sources

$PT$ Symmetric PINN for integrable nonlocal equations: Forward and inverse problems [PDF]

Since the $PT$-symmetric nonlocal equations contain the physical information of the $PT$-symmetric, it is very appropriate to embed the physical information of the $PT$-symmetric into the loss function of PINN, named PTS-PINN.
Peng, Wei-Qi, Chen, Yong
core   +1 more source

MultiPINN: A Modular Framework for Physics-Informed Neural Networks

Machine Learning and Knowledge Extraction
Physics-informed neural networks (PINNs) approximate solutions of differential equations by embedding governing equations and boundary conditions into the training objective.
Chermen Tsgoev, Vladislav Kalinin, Miron Bratenkov, Alexander Seredkin, Danil Sakharov, Vlad Travnikov, Danil Kasyanov, Mikhail Matushkin, Alexander Nekriach, Dmitriy Fomichev, Rustam Mullyadzhanov   +10 more
doaj   +1 more source

Adaptive Hybrid B-Spline PINN for Fractional Equations

We propose a hybrid numerical framework combining a redefined quintic B-spline collocation method with a Physics-Informed Neural Network (PINN) correction layer for solving nonlinear space-time fractional advection-diffusion-reaction equations.
Tumbanath Samantara, Prabhu Sankardarshi Mahapatra, Balaji Padhy   +2 more
core   +1 more source

A Mixed-Form PINNS (MF-PINNS) For Solving The Coupled Stokes-Darcy Equations

Parallel physical information neural networks (P-PINNs) have been widely used to solve systems with multiple coupled physical fields, such as the coupled Stokes-Darcy equations with Beavers-Joseph-Saffman (BJS) interface conditions. However, excessively high or low physical constants in partial differential equations (PDE) often lead to ill ...
Shan, Li, Shen, Xi
openaire   +2 more sources

Advanced PINN Integration with Multiple PINN Methods

This thesis evaluates the efficacy of Physics-Informed Neural Networks (PINNs) in simulating fluid dynamics challenges, focusing on the Burgers' equation and the lid-driven cavity problem, to develop a robust PINN framework for nuclear engineering applications such as the Sustainable Nuclear Energy Research In Sweden (SUNRISE) project.
openaire   +1 more source

A Control Perspective on Training PINNs

We investigate the training of Physics-Informed Neural Networks (PINNs) from a control-theoretic perspective. Using gradient descent with resampling, we interpret the training dynamics as asymptotically equivalent to a stochastic control-affine system, where sampling effects act as process disturbances and measurement noise.
Barreau, Matthieu, Shen, Haoming
openaire   +2 more sources