Exploring Physics-Informed Neural Networks for the Generalized Nonlinear Sine-Gordon Equation DOI Creative Commons
Alemayehu Tamirie Deresse, Tamirat Temesgen Dufera

Applied Computational Intelligence and Soft Computing, Journal Year: 2024, Volume and Issue: 2024(1)

Published: Jan. 1, 2024

The nonlinear sine‐Gordon equation is a prevalent feature in numerous scientific and engineering problems. In this paper, we propose machine learning‐based approach, physics‐informed neural networks (PINNs), to investigate explore the solution of generalized non‐linear equation, encompassing Dirichlet Neumann boundary conditions. To incorporate physical information for multiobjective loss function has been defined consisting residual governing partial differential (PDE), initial conditions, various Using multiple densely connected independent artificial (ANNs), called feedforward deep designed handle equations, PINNs have trained through automatic differentiation minimize that incorporates given PDE governs laws phenomena. illustrate effectiveness, validity, practical implications our proposed two computational examples from are presented. We developed PINN algorithm implemented it using Python software. Various experiments were conducted determine an optimal architecture. network training was employed by current state‐of‐the‐art optimization methods learning known as Adam L‐BFGS‐B minimization techniques. Additionally, solutions method compared with established analytical found literature. findings show approach accurate efficient solving equations variety conditions well any complex problems across disciplines.

Language: Английский

DenseSPH-YOLOv5: An automated damage detection model based on DenseNet and Swin-Transformer prediction head-enabled YOLOv5 with attention mechanism DOI

Arunabha M. Roy,

Jayabrata Bhaduri

Advanced Engineering Informatics, Journal Year: 2023, Volume and Issue: 56, P. 102007 - 102007

Published: April 1, 2023

Language: Английский

Citations

163
An efficient and robust Phonocardiography (PCG)-based Valvular Heart Diseases (VHD) detection framework using Vision Transformer (ViT) DOI
Sonain Jamil,

Arunabha M. Roy

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 158, P. 106734 - 106734

Published: March 1, 2023

Language: Английский

Citations

66
Physics-informed Neural Networks (PINN) for computational solid mechanics: Numerical frameworks and applications DOI

Haoteng Hu,

Lehua Qi, Xujiang Chao

et al.

Thin-Walled Structures, Journal Year: 2024, Volume and Issue: 205, P. 112495 - 112495

Published: Sept. 24, 2024

Language: Английский

Citations

46
Application of Machine Learning and Deep Learning in Finite Element Analysis: A Comprehensive Review DOI
Dipjyoti Nath,

Ankit,

Debanga Raj Neog

et al.

Archives of Computational Methods in Engineering, Journal Year: 2024, Volume and Issue: 31(5), P. 2945 - 2984

Published: March 1, 2024

Language: Английский

Citations

29
Physics-informed kernel function neural networks for solving partial differential equations DOI
Zhuojia Fu, Wenzhi Xu,

Shuainan Liu

et al.

Neural Networks, Journal Year: 2024, Volume and Issue: 172, P. 106098 - 106098

Published: Jan. 2, 2024

Language: Английский

Citations

27
Differentiable finite element method with Galerkin discretization for fast and accurate inverse analysis of multidimensional heterogeneous engineering structures DOI Creative Commons
Xi Wang, Zhen‐Yu Yin, Wei Wu

et al.

Computer Methods in Applied Mechanics and Engineering, Journal Year: 2025, Volume and Issue: 437, P. 117755 - 117755

Published: Jan. 22, 2025

Language: Английский

Citations

6
Physics-informed deep neural network for modeling the chloride diffusion in concrete DOI
Wafaa Mohamed Shaban, Khalid Elbaz, Annan Zhou

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 125, P. 106691 - 106691

Published: July 5, 2023

Language: Английский

Citations

31
A conceptual metaheuristic-based framework for improving runoff time series simulation in glacierized catchments DOI Creative Commons
Babak Mohammadi, Saeed Vazifehkhah, Zheng Duan

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 127, P. 107302 - 107302

Published: Nov. 8, 2023

Glacio-hydrological modeling is a key task for assessing the influence of snow and glaciers on water resources, essential resources management. The present study aims to enhance conceptual hydrological model (namely Glacial Snow Melt (GSM)) by data-driven swarm computing enhancing accuracy rainfall runoff prediction. proposed framework combines (i.e. GSM) with time series predictor (SVR) optimization-driven parameter tuning firefly algorithm (SVR-FFA). This integration uniquely captures complex interplay between meteorological variables, glacier processes, responses. Applying hybrid proved better results than standalone GSM ordinary SVR in simulating series. performance integrated metaheuristic-based (W-SG-SVR-FFA) demonstrated several enhancements over model. During calibration (validation) period, evaluation metric coefficient determination (R2) was 0.77 (0.77) 0.98 (0.91) W-SG-SVR-FFA Kling-Gupta Efficiency (KGE) values were 0.81 0.97 (0.87), respectively. method glacierized catchments underscores its importance areas undergoing swift climate change glacial melting. approach enables readers witness intricate equilibrium model's complexity simulation outcomes.

Language: Английский

Citations

27
A physics informed bayesian optimization approach for material design: application to NiTi shape memory alloys DOI Creative Commons
Danial Khatamsaz,

Raymond Neuberger,

Arunabha M. Roy

et al.

npj Computational Materials, Journal Year: 2023, Volume and Issue: 9(1)

Published: Dec. 13, 2023

Abstract The design of materials and identification optimal processing parameters constitute a complex challenging task, necessitating efficient utilization available data. Bayesian Optimization (BO) has gained popularity in due to its ability work with minimal However, many BO-based frameworks predominantly rely on statistical information, the form input-output data, assume black-box objective functions. In practice, designers often possess knowledge underlying physical laws governing material system, rendering function not entirely black-box, as some information is partially observable. this study, we propose physics-informed BO approach that integrates physics-infused kernels effectively leverage both decision-making process. We demonstrate method significantly improves efficiency enables more data-efficient BO. applicability showcased through NiTi shape memory alloys, where are identified maximize transformation temperature.

Language: Английский

Citations

27
The Application of Physics-Informed Machine Learning in Multiphysics Modeling in Chemical Engineering DOI
Zhi‐Yong Wu, Huan Wang, Chang He

et al.

Industrial & Engineering Chemistry Research, Journal Year: 2023, Volume and Issue: 62(44), P. 18178 - 18204

Published: Oct. 26, 2023

Physics-Informed Machine Learning (PIML) is an emerging computing paradigm that offers a new approach to tackle multiphysics modeling problems prevalent in the field of chemical engineering. These often involve complex transport processes, nonlinear reaction kinetics, and coupling. This Review provides detailed account main contributions PIML with specific emphasis on momentum transfer, heat mass reactions. The progress method development (e.g., algorithm architecture), software libraries, applications coupling surrogate modeling) are detailed. On this basis, future challenges highlight importance developing more practical solutions strategies for PIML, including turbulence models, domain decomposition, training acceleration, modeling, hybrid geometry module creation.

Language: Английский

Citations

26