Cited by Fast prediction of three-dimensional indoor flow fields by a reduced dimensional deep-learning approach

A rapid unsaturated infiltration prediction method for slope stability analysis considering uncertainties: Deep operator networks DOI

Peng Lan, Jinsong Huang, Jingjing Su

et al.

Engineering Geology, Journal Year: 2025, Volume and Issue: unknown, P. 107886 - 107886

Published: Jan. 1, 2025

Language: Английский

Citations

Towards the use of data-driven methods for indoor airflow field reconstruction: A systematic review DOI

A. Olivas,

Jurng‐Jae Yee

Journal of Building Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 112581 - 112581

Published: April 1, 2025

Language: Английский

Citations

Physics-informed neural networks (P INNs): application categories, trends and impact DOI

Mohammad Ghalambaz, Mikhail А. Sheremet, Mohammed Arshad Khan

et al.

International Journal of Numerical Methods for Heat &amp Fluid Flow, Journal Year: 2024, Volume and Issue: 34(8), P. 3131 - 3165

Published: July 10, 2024

Purpose This study aims to explore the evolving field of physics-informed neural networks (PINNs) through an analysis 996 records retrieved from Web Science (WoS) database 2019 2022. Design/methodology/approach WoS was analyzed for PINNs using inhouse python code. The author’s collaborations, most contributing institutes, countries and journals were identified. trends application categories also analyzed. Findings papers classified into seven key domains: Fluid Dynamics computational fluid dynamics (CFD); Mechanics Material Science; Electromagnetism Wave Propagation; Biomedical Engineering Biophysics; Quantum Physics; Renewable Energy Power Systems; Astrophysics Cosmology. CFD emerged as primary focus, accounting 69.3% total publications witnessing exponential growth 22 in 366 followed, with impressive trajectory 3 65 within same period. underscored rising interest across diverse fields such Biophysics, Systems. Furthermore, focus active each category examined, revealing, instance, USA’s significant contribution 319 66 papers. Originality/value illuminates rapidly expanding role tackling complex scientific problems highlights its potential future research domains.

Language: Английский

Citations

Exploration of deep operator networks for predicting the piezoionic effect DOI

Shuyu Wang,

Dingli Zhang,

A.H.-J. Wang

et al.

The Journal of Chemical Physics, Journal Year: 2025, Volume and Issue: 162(11)

Published: March 17, 2025

The piezoionic effect holds significant promise for revolutionizing biomedical electronics and ionic skins. However, modeling this multiphysics phenomenon remains challenging due to its high complexity computational limitations. To address problem, study pioneers the application of deep operator networks effectively model time-dependent effect. By leveraging a data-driven approach, our significantly reduces time compared traditional finite element analysis (FEA). In particular, we trained DeepONet using comprehensive dataset generated through FEA calibrated experimental data. Through rigorous testing with step responses, slow-changing forces, dynamic-changing show that captures intricate temporal dynamics in both horizontal vertical planes. This capability offers powerful tool real-time phenomena, contributing simplifying design tactile interfaces potentially complementing existing imaging technologies.

Language: Английский

Citations

Evaluation of supervised machine learning regression models for CFD-based surrogate modelling in indoor airflow field reconstruction DOI