Results 261 to 270 of about 111,744 (341)

A highly accurate numerical method for solving boundary value problem of generalized Bagley‐Torvik equation

open access: yesMathematical Methods in the Applied Sciences, EarlyView.
A highly accurate numerical method is given for the solution of boundary value problem of generalized Bagley‐Torvik (BgT) equation with Caputo derivative of order 0<β<2$$ 0<\beta <2 $$ by using the collocation‐shooting method (C‐SM). The collocation solution is constructed in the space Sm+1(1)$$ {S}_{m+1}^{(1)} $$ as piecewise polynomials of degree at ...
Suzan Cival Buranay   +2 more
wiley   +1 more source

The Neuromusculoskeletal Modeling Pipeline: MATLAB-based model personalization and treatment optimization functionality for OpenSim. [PDF]

open access: yesJ Neuroeng Rehabil
Hammond CV   +10 more
europepmc   +1 more source

SCT‐BEM for Transient Heat Conduction and Wave Propagation in 2D Thin‐Walled Structures

open access: yesInternational Journal of Mechanical System Dynamics, EarlyView.
ABSTRACT Traditional boundary element method (BEM) faces significant challenges in addressing dynamic problems in thin‐walled structures. These challenges arise primarily from the complexities of handling time‐dependent terms and nearly singular integrals in structures with thin‐shapes.
Xiaotong Gao, Yan Gu
wiley   +1 more source

Transfer Learning in Physics‐Informed Neurals Networks: Full Fine‐Tuning, Lightweight Fine‐Tuning, and Low‐Rank Adaptation

open access: yesInternational Journal of Mechanical System Dynamics, EarlyView.
ABSTRACT AI for PDEs has garnered significant attention, particularly physics‐informed neural networks (PINNs). However, PINNs are typically limited to solving specific problems, and any changes in problem conditions necessitate retraining. Therefore, we explore the generalization capability of transfer learning in the strong and energy forms of PINNs ...
Yizheng Wang   +6 more
wiley   +1 more source

Numerical Simulation of Transient Heat Conduction With Moving Heat Source Using Physics Informed Neural Networks

open access: yesInternational Journal of Mechanical System Dynamics, EarlyView.
ABSTRACT In this article, the physics informed neural networks (PINNs) is employed for the numerical simulation of heat transfer involving a moving source under mixed boundary conditions. To reduce computational effort and increase accuracy, a new training method is proposed that uses a continuous time‐stepping through transfer learning.
Anirudh Kalyan, Sundararajan Natarajan
wiley   +1 more source

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