Building and Environment, Год журнала: 2024, Номер unknown, С. 112517 - 112517
Опубликована: Дек. 1, 2024
Язык: Английский
Building and Environment, Год журнала: 2024, Номер unknown, С. 112517 - 112517
Опубликована: Дек. 1, 2024
Язык: Английский
Engineering Geology, Год журнала: 2025, Номер unknown, С. 107886 - 107886
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
2Advances in Applied Energy, Год журнала: 2025, Номер unknown, С. 100223 - 100223
Опубликована: Май 1, 2025
Язык: Английский
Процитировано
0International Journal of Numerical Methods for Heat & Fluid Flow, Год журнала: 2024, Номер 34(8), С. 3131 - 3165
Опубликована: Июль 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.
Язык: Английский
Процитировано
3Building and Environment, Год журнала: 2024, Номер unknown, С. 112173 - 112173
Опубликована: Окт. 1, 2024
Язык: Английский
Процитировано
3The Journal of Chemical Physics, Год журнала: 2025, Номер 162(11)
Опубликована: Март 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.
Язык: Английский
Процитировано
0Journal of Building Engineering, Год журнала: 2025, Номер unknown, С. 112581 - 112581
Опубликована: Апрель 1, 2025
Язык: Английский
Процитировано
0Building and Environment, Год журнала: 2025, Номер unknown, С. 113102 - 113102
Опубликована: Май 1, 2025
Язык: Английский
Процитировано
0Building and Environment, Год журнала: 2025, Номер unknown, С. 113246 - 113246
Опубликована: Май 1, 2025
Язык: Английский
Процитировано
0Sustainable Energy Technologies and Assessments, Год журнала: 2024, Номер 66, С. 103823 - 103823
Опубликована: Май 30, 2024
Язык: Английский
Процитировано
1Thermal Science and Engineering Progress, Год журнала: 2024, Номер 55, С. 102946 - 102946
Опубликована: Сен. 30, 2024
Язык: Английский
Процитировано
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