Results 111 to 120 of about 2,291 (214)

From Gauged to Ungauged: Using Catchment Morphology to Guide the Transfer of Physics‐Informed Neural Network Discharge Predictions

Hydrological Processes, Volume 40, Issue 6, June 2026.
This study develops a transferability framework that combines ensemble clustering with hybrid PINN‐GRU models for discharge prediction in ungauged catchments. Using 10 catchment morphometrics, 117 subcatchments were grouped into four clusters, achieving strong within‐cluster transferability.
Mehran Khan, Kirstie Fryirs, Lucy Marshall   +2 more
wiley   +1 more source

Analytical and Numerical Soliton Solutions of the Shynaray II‐A Equation Using the G′G,1G$$ \left(\frac{G^{\prime }}{G},\frac{1}{G}\right) $$‐Expansion Method and Regularization‐Based Neural Networks

Mathematical Methods in the Applied Sciences, Volume 49, Issue 9, Page 9814-9831, June 2026.
ABSTRACT Nonlinear differential equations play a fundamental role in modeling complex physical phenomena across solid‐state physics, hydrodynamics, plasma physics, nonlinear optics, and biological systems. This study focuses on the Shynaray II‐A equation, a relatively less‐explored parametric nonlinear partial differential equation that describes ...
Aamir Farooq, H. W. A. Riaz, Sadique Rehman, M. Mamun Miah, Wen Xiu Ma   +4 more
wiley   +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

Physics-Informed Neural Network for Nonlinear Bending Analysis of Nano-Beams: A Systematic Hyperparameter Optimization

Mathematical and Computational Applications
This paper investigates the nonlinear bending analysis of nano-beams using the physics-informed neural network (PINN) method. The nonlinear governing equations for the bending of size-dependent nano-beams are derived from Hamilton’s principle ...
Saba Sadat Mirsadeghi Esfahani, Ali Fallah, Mohammad Mohammadi Aghdam   +2 more
doaj   +1 more source

Elastoplasticity Informed Kolmogorov–Arnold Networks Using Chebyshev Polynomials

International Journal for Numerical and Analytical Methods in Geomechanics, Volume 50, Issue 8, Page 3331-3354, 10 June 2026.
ABSTRACT Multilayer perceptron (MLP) networks are predominantly used to develop data‐driven constitutive models for granular materials. They offer a compelling alternative to traditional physics‐based constitutive models in predicting non‐linear responses of these materials, for example, elastoplasticity, under various loading conditions. To attain the
Farinaz Mostajeran, Salah A. Faroughi
wiley   +1 more source

Non-Invasive Breast Cancer Detection Using Physics Informed Neural Networks with Thermal Imaging and 3D Patient-Specific Breast Models

Engineered Science
This study presents a novel non-invasive approach for breast cancer detection and tumor localization by developing an optimized Physics-Informed Neural Network (PINN) model integrated with infrared (IR) thermal imaging and 3D physical breast modeling ...
Olzhas Mukhmetov, Yong Zhao, Aigerim Mashekova, Dongming Wei, Vasilios Zarikas, Eddie YK Ng, Amgad Salama, Madina Shapatova, Nurduman Aidossov   +8 more
doaj   +1 more source

On the Performance and Convergence of PINNs for Problems in Linear Elasticity

Proceedings in Applied Mathematics and Mechanics, Volume 26, Issue 2, June 2026.
ABSTRACT Physics‐informed neural networks (PINNs) have emerged as a promising approach for solving partial differential equations by embedding physical laws directly into the loss function. However, their performance characteristics for problems in computational mechanics remain insufficiently understood.
Dipraj Kadlag, Carolin Birk, Sundararajan Natarajan, Hauke Gravenkamp   +3 more
wiley   +1 more source

Novel Method to Improve the Convergence of Physics-Informed Neural Networks for Complex Thermal Simulations

Applied Sciences
In the context of developing PINN methods for real-time digital twins in manufacturing processes, we propose a new approach that combines two complementary weighting strategies to significantly improve their convergence. The first method, called SD-PINN,
Amèvi Tongne, Lionel Arnaud
doaj   +1 more source

Programmable Fabric‐Based Soft Pneumatic Actuators for Wearables: A Review

Small Structures, Volume 7, Issue 6, June 2026.
This review summarizes recent advances in fabric soft pneumatic actuators (FSPAs), emphasizing programmable design strategies, diverse actuation deformation modes, material selection, and emerging applications. We highlight how geometric constraints, material distribution, and specialized fabrication methods govern longitudinal, bending, torsional, and
Zichao Ling, Zichun Cao, Shanshan Dong, Jie Zhang, Yadie Yang   +4 more
wiley   +1 more source

An Overview of Artificial Intelligence and Machine Learning Approaches for Building Energy Analysis, Characterization, Control, and Grid Support Services Provision

WIREs Energy and Environment, Volume 15, Issue 2, June 2026.
Artificial Intelligence and Machine Learning Approaches used in Building Energy Analysis, Control, and Provision of Grid Support Services. ABSTRACT Increasing penetrations of variable renewable energy sources like wind and solar photovoltaic (PV) systems are challenging power system stability worldwide.
Jack S. Bryant, Hangxin Li, Nawanjana Maheepala, Xiaoyu Lin, Mingkun Dai, Binglong Han, Robin Guan, Lasantha Meegahapola, Shengwei Wang, Dilan Robert, Liuping Wang, Fu Xiao   +11 more
wiley   +1 more source