Results 71 to 80 of about 4,137 (225)

A Physics Constrained Machine Learning Pipeline for Young's Modulus Prediction in Multimaterial Hyperelastic Cylinders Guided by Contact Mechanics

Advanced Intelligent Discovery, EarlyView.
A physics‐guided machine learning framework estimates Young's modulus in multilayered multimaterial hyperelastic cylinders using contact mechanics. A semiempirical stiffness law is embedded into a custom neural network, ensuring physically consistent predictions. Validation against experimental and numerical data on C.
Christoforos Rekatsinas, Panagiotis Krokidas, Vasileios Vavourakis, Clara Essmann, George Giannakopoulos   +4 more
wiley   +1 more source

Dynamics of Soliton Solutions to Nonlinear Dynamical Equations in Mathematical Physics: Application of Neural Network-Based Symbolic Methods

Mathematics
While recent advances have successfully integrated neural networks with physical models to derive numerical solutions, there remains a compelling need to obtain exact analytical solutions.
Jan Muhammad, Aljethi Reem Abdullah, Fengping Yao, Usman Younas   +3 more
doaj   +1 more source

Gaussian Process Regression–Neural Network Hybrid with Optimized Redundant Coordinates: A New Simple Yet Potent Tool for Scientist's Machine Learning Toolbox

Advanced Intelligent Discovery, EarlyView.
A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley   +1 more source

Traveling synchronized asymmetric two-waves in the propagation of the KdV and mKdV equations incorporating time-space dispersion terms

Partial Differential Equations in Applied Mathematics
Joseph and Egri revised the standard Korteweg-de Vries equation by replacing its third-order space dispersion term by space-time dispersions aiming to adjust the wave speed and preserve frequency stability. The aim of the current study is twofold. First,
Marwan Alquran, Imad Jaradat
doaj   +1 more source

RAMS: Residual‐Based Adversarial‐Gradient Moving Sample Method for Scientific Machine Learning in Solving Partial Differential Equations

Advanced Intelligent Discovery, EarlyView.
We propose a residual‐based adversarial‐gradient moving sample (RAMS) method for scientific machine learning that treats samples as trainable variables and updates them to maximize the physics residual, thereby effectively concentrating samples in inadequately learned regions.
Weihang Ouyang, Min Zhu, Wei Xiong, Si‐Wei Liu, Lu Lu   +4 more
wiley   +1 more source

Interaction Solutions for the Fractional KdVSKR Equations in (1+1)-Dimension and (2+1)-Dimension

Fractal and Fractional
We extend two KdVSKR models to fractional KdVSKR models with the Caputo derivative. The KdVSKR equation in (2+1)-dimension, which is a recent extension of the KdVSKR equation in (1+1)-dimension, can model the soliton resonances in shallow water. Applying
Lihua Zhang, Zitong Zheng, Bo Shen, Gangwei Wang, Zhenli Wang   +4 more
doaj   +1 more source

Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning

Advanced Intelligent Discovery, EarlyView.
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley   +1 more source

Probabilistic Forecast of Multiphase Transport Under Viscous and Buoyancy Forces in Heterogeneous Porous Media

Water Resources Research
We develop a probabilistic approach to map parametric uncertainty to output state uncertainty in first‐order hyperbolic conservation laws. We analyze this problem for nonlinear immiscible two‐phase transport in heterogeneous porous media in the presence ...
Farzaneh Rajabi, Hamdi A. Tchelepi
doaj   +1 more source

Extending Battery Usage Time of a Heavy‐Duty Mecanum‐Wheeled Autonomous Electric Vehicle Used in Iron–Steel Industry by Considering Wheel Slippage

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
It is a fact that slippage causes tracking errors in both longitudinal and lateral directions which results to have less travel distance in tracking a reference trajectory. Less travel distance means having energy loss of the battery and carrying loads less than planned.
Gokhan Bayar, Goktug Hambarci, Ali Sezai Demirel   +2 more
wiley   +1 more source

Introduction of a Simple Cnoidal Wave Formulation Based on Nonlinear Interaction of Wave-Wave Principles

International Journal of Coastal and Offshore Engineering, 2017
In this study, a simple and efficient approach based on nonlinear wave interaction fundamentals is theoretically proposed to generate surface profile of the cnoidal waves.
Seyed Masoud Mahmoudof, Fatemeh Hajivalie   +1 more
doaj