Integrating symbolic regression with physics-informed neural networks for simulating nonlinear wave dynamics in arterial blood flow DOI
Satyasaran Changdar, Bivas Bhaumik,

Nabanita Sadhukhan

и другие.

Physics of Fluids, Год журнала: 2024, Номер 36(12)

Опубликована: Дек. 1, 2024

This study explores a hybrid framework integrating machine learning techniques and symbolic regression via genetic programing for analyzing the nonlinear propagation of waves in arterial blood flow. We employ mathematical to simulate viscoelastic flow, incorporating assumptions long wavelength large Reynolds numbers. used fifth-order evolutionary equation using reductive perturbation represent behavior tube, considering tube wall's bending. obtain solutions through physics-informed neural networks (PINNs) that optimizes Bayesian hyperparameter optimization across three distinct initial conditions. found PINN-based models are proficient at predicting higher-order partial differential equations spatial-temporal domain [−1,1]×[0,2]. is evidenced by graphical results residual validation showing mean absolute residue error O(10−3). thoroughly examine impacts various Furthermore, combined into single model random forest algorithm, achieving an impressive accuracy 99% on testing dataset compared with another artificial network. Finally, analytical form estimated provides interpretable square These insights contribute interpretation cardiovascular parameters, potentially advancing applications within medical domain.

Язык: Английский

Loss-attentional physics-informed neural networks DOI Creative Commons
Yanjie Song, He Wang, Yang He

и другие.

Journal of Computational Physics, Год журнала: 2024, Номер 501, С. 112781 - 112781

Опубликована: Янв. 20, 2024

Physics-informed neural networks (PINNs) have emerged as a significant endeavor in recent years to utilize artificial intelligence technology for solving various partial differential equations (PDEs). Nevertheless, the vanilla PINN model structure encounters challenges accurately approximating solutions at hard-to-fit regions with, instance, "stiffness" points characterized by fast-paced alterations timescale. To this end, we introduce novel architecture based on PINN, named loss-attentional physics-informed (LA-PINN), which equips each loss component with an independent network (LAN). Feeding squared errors (SE) every training point into LAN input, attentional function is then built and provides different weights diverse SEs. A error-based weighting approach that utilizes adversarial between multiple LA-PINN proposed dynamically update of SE during epoch. Additionally, mechanism analysed also be validated performing several numerical experiments. The experimental results indicate method displays superior predictive performance compared holds swift convergence characteristic. Moreover, it can advance those progressively increasing growth rates both weight gradient error.

Язык: Английский

Процитировано

17
When geoscience meets generative AI and large language models: Foundations, trends, and future challenges DOI Creative Commons
Abdenour Hadid, Tanujit Chakraborty,

D. Busby

и другие.

Expert Systems, Год журнала: 2024, Номер 41(10)

Опубликована: Июнь 11, 2024

Abstract Generative Artificial Intelligence (GAI) represents an emerging field that promises the creation of synthetic data and outputs in different modalities. GAI has recently shown impressive results across a large spectrum applications ranging from biology, medicine, education, legislation, computer science, finance. As one strives for enhanced safety, efficiency, sustainability, generative AI indeed emerges as key differentiator paradigm shift field. This article explores potential language models geoscience. The recent developments machine learning deep have enabled model's utility tackling diverse prediction problems, simulation, multi‐criteria decision‐making challenges related to geoscience Earth system dynamics. survey discusses several been used comprising adversarial networks (GANs), physics‐informed neural (PINNs), pre‐trained transformer (GPT)‐based structures. These tools helped community applications, including (but not limited to) generation/augmentation, super‐resolution, panchromatic sharpening, haze removal, restoration, land surface changing. Some still remain, such ensuring physical interpretation, nefarious use cases, trustworthiness. Beyond that, show community, especially with support climate change, urban atmospheric marine planetary science through their extraordinary ability data‐driven modelling uncertainty quantification.

Язык: Английский

Процитировано

13
A systematic and bibliometric review on physics-based neural networks applications as a solution for structural engineering partial differential equations DOI
Ahed Habib, Ausamah AL Houri, M. Talha Junaid

и другие.

Structures, Год журнала: 2024, Номер 69, С. 107361 - 107361

Опубликована: Окт. 1, 2024

Язык: Английский

Процитировано

6
Physics-informed neural network combined with characteristic-based split for solving forward and inverse problems involving Navier–Stokes equations DOI
Shuang Hu, Meiqin Liu, Senlin Zhang

и другие.

Neurocomputing, Год журнала: 2024, Номер 573, С. 127240 - 127240

Опубликована: Янв. 9, 2024

Язык: Английский

Процитировано

4
Shallow-water problems DOI

O. C. Zienkiewicz,

Robert L. Taylor, Perumal Nithiarasu

и другие.

Elsevier eBooks, Год журнала: 2025, Номер unknown, С. 325 - 349

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Hybrid physics‐informed neural network with parametric identification for modeling bridge temperature distribution DOI Creative Commons
Yanjia Wang,

Dong Ho Yang,

Ye Yuan

и другие.

Computer-Aided Civil and Infrastructure Engineering, Год журнала: 2025, Номер unknown

Опубликована: Фев. 18, 2025

Abstract This paper introduces a novel hybrid multi‐model thermo‐temporal physics‐informed neural network (TT‐PINN) framework for thermal loading prediction in composite bridge decks. Unlike the existing PINN applications heat transfer that focus on simple geometries, this uniquely addresses multi‐material domains and realistic boundary conditions through dual‐network architecture designed structures. The further incorporates environmental of natural convection solar radiation into loss function employs learning efficient adaptation to varying conditions. Moreover, mechanism enables rapid new states, thus markedly reducing computations as compared conventional finite element method (FEM). Through noise‐augmented training parameter identification, TT‐PINN effectively handles real‐world monitoring data uncertainties allows material property calibration with limited sensor data. framework's ability capture complex behavior is validated by studying cable‐stayed bridge. It significantly reduces computational costs traditional FEM approaches.

Язык: Английский

Процитировано

0
Modeling on magnetohydrodynamic Stokes flow using machine learning and curve fitting DOI Creative Commons
Merve Gürbüz, Bengisen Pekmen Geridönmez

Neural Computing and Applications, Год журнала: 2025, Номер unknown

Опубликована: Март 5, 2025

Язык: Английский

Процитировано

0
Physics-Informed Neural Networks for the Structural Analysis and Monitoring of Railway Bridges: A Systematic Review DOI Creative Commons
Yuniel Ernesto Martínez Pérez, Luis Rojas, Álvaro Peña

и другие.

Mathematics, Год журнала: 2025, Номер 13(10), С. 1571 - 1571

Опубликована: Май 10, 2025

Physics-informed neural networks (PINNs) offer a mesh-free approach to solving partial differential equations (PDEs) with embedded physical constraints. Although PINNs have gained traction in various engineering fields, their adoption for railway bridge analysis remains under-explored. To address this gap, systematic review was conducted across Scopus and Web of Science (2020–2025), filtering records by relevance, journal impact, language. From an initial pool, 120 articles were selected categorised into nine thematic clusters that encompass computational frameworks, hybrid integration conventional solvers, domain decomposition strategies. Through natural language processing (NLP) trend mapping, evidences growing but fragmented research landscape. demonstrate promising capabilities load distribution modelling, structural health monitoring, failure prediction, particularly under dynamic train loads on multi-span bridges. However, methodological gaps persist large-scale simulations, plasticity experimental validation. Future work should focus scalable PINN architectures, refined modelling inelastic behaviours, real-time data assimilation, ensuring robustness generalisability through interdisciplinary collaboration.

Язык: Английский

Процитировано

0
Data-intelligence driven methods for durability, damage diagnosis and performance prediction of concrete structures DOI Creative Commons
Fan Li, Daming Luo, Ditao Niu

и другие.

Communications Engineering, Год журнала: 2025, Номер 4(1)

Опубликована: Июнь 3, 2025

A large number of in-service reinforced concrete structures are now entering the mid-to-late stages their service life. Efficient detection damage characteristics and accurate prediction material performance degradation have become essential for ensuring safety these structures. Traditional methods, which primarily rely on manual inspections sensor monitoring, inefficient lack accuracy. Similarly, models materials, often based limited experimental data polynomial fitting, oversimplify influencing factors. In contrast, partial differential equation that account mechanisms computationally intensive difficult to solve. Recent advancements in deep learning machine learning, as part artificial intelligence, introduced innovative approaches both This paper provides a comprehensive overview theories models, reviews current research application durability structures, focusing two main areas: intelligent predictive modeling durability. Finally, article discusses future trends offers insights into innovation structure

Язык: Английский

Процитировано

0
A least squares–support vector machine for learning solution to multi-physical transient-state field coupled problems DOI

Xiaoming Han,

Xin Zhao,

Yecheng Wu

и другие.

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 138, С. 109321 - 109321

Опубликована: Сен. 20, 2024

Язык: Английский

Процитировано

2