Results 131 to 140 of about 2,349 (245)

Closing the Loop in Precision Oncology: A Digital Twin‐Driven Paradigm for Dynamic Decision‐Making

iMetaMed, Volume 2, Issue 2, June 2026.
This review introduces the Closed‐Loop Intelligent Oncology System (CIOS), a five‐layer framework integrating digital twins and AI to enable adaptive, data‐driven cancer treatment. By synthesizing advances in multimodal perception, mechanistic simulation, and safe reinforcement learning, CIOS charts a roadmap toward dynamic, personalized oncology ...
Junye Zhu, Mengqi Yang, Chenxi Ouyang, Lin Zhang   +3 more
wiley   +1 more source

SWENet: A Physics-Informed Deep Neural Network (PINN) for Shear Wave Elastography

IEEE Transactions on Medical Imaging
Shear wave elastography (SWE) enables the measurement of elastic properties of soft materials, including soft tissues, in a non-invasive manner and finds broad applications in a variety of disciplines. The state-of-the-art SWE methods commercialized in various instruments rely on the measurement of shear wave velocities to infer material parameters and
Ziying Yin, Guoyang Li, Zhaoyi Zhang, Yang Zheng, Yanping Cao   +4 more
openaire   +3 more sources

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

Physics-Informed Neural Networks for Structural Mechanics and Construction: Modeling the Deflection of a Single-Span Beam Physics-Informed Neural Networks

Железобетонные конструкции
This article presents the development and analysis of a Physics-Informed Neural Network (PINN) model for calculating the deflection of a simply supported beam under uniformly distributed load.
F. N. Zakharov, Qian Jie, Xu Yi
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

Space Correlation Constrained Physics Informed Neural Network for Seismic Tomography

Journal of Geophysical Research: Machine Learning and Computation
Physics‐informed neural networks (PINNs) integrate physical constraints with neural architectures and leverage their nonlinear fitting capabilities to solve complex inverse problems.
Yonghao Wang, Zhuo Jia, Bowu Jiang, Wenkai Lu   +3 more
doaj   +1 more source

A hybrid A-UKF-PINN digital twin architecture for real-time state estimation in Smart Grids

Ìнформаційні технології та компʼютерна інженерія
The increasing variability, nonlinearity, and real-time operational requirements of Smart Grids (SGs) make static digital models insufficient for reliable state estimation and control of distributed assets such as Vehicle-to-Grid (V2G) storage systems ...
V. Vychuzhanin, A. Vychuzhanin
doaj   +1 more source

Δ-PINNs: Physics-informed neural networks on complex geometries

Engineering Applications of Artificial Intelligence
Physics-informed neural networks (PINNs) have demonstrated promise in solving forward and inverse problems involving partial differential equations. Despite recent progress on expanding the class of problems that can be tackled by PINNs, most of existing use-cases involve simple geometric domains.
Francisco Sahli Costabal, Simone Pezzuto, Paris Perdikaris   +2 more
openaire   +4 more sources

Mixed-depth physics-informed neural network with nested activation mechanism in solving partial differential equations

Physics-informed neural networks (PINNs) have become promising tools for solving complex partial differential equations (PDEs), but traditional PINNs suffered from slow convergence, vanishing gradients, and poor handling of local physical features.
Xu, Xu, Liu, Guirong, Li, Eric; id_orcid, Wang, Tianhao   +3 more
core   +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