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

Engineering Geology, Год журнала: 2025, Номер unknown, С. 107886 - 107886

Опубликована: Янв. 1, 2025

Язык: Английский

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Physics-informed machine learning for building performance simulation-A review of a nascent field

Advances in Applied Energy, Год журнала: 2025, Номер unknown, С. 100223 - 100223

Опубликована: Май 1, 2025

Язык: Английский

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

International Journal of Numerical Methods for Heat &amp 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

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

Building and Environment, Год журнала: 2024, Номер unknown, С. 112173 - 112173

Опубликована: Окт. 1, 2024

Язык: Английский

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Exploration of deep operator networks for predicting the piezoionic effect

The 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

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

Journal of Building Engineering, Год журнала: 2025, Номер unknown, С. 112581 - 112581

Опубликована: Апрель 1, 2025

Язык: Английский

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Development of a scripting tool for the fast and batch generation of orthogonal hexahedral mesh for CFD analysis in built environments

Building and Environment, Год журнала: 2025, Номер unknown, С. 113102 - 113102

Опубликована: Май 1, 2025

Язык: Английский

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Applying a physics-informed neural network to an indoor airflow time-extrapolation prediction

Building and Environment, Год журнала: 2025, Номер unknown, С. 113246 - 113246

Опубликована: Май 1, 2025

Язык: Английский

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Predictive monitoring of built thermal environment using limited sensor data: A deep learning-based spatiotemporal method

Sustainable Energy Technologies and Assessments, Год журнала: 2024, Номер 66, С. 103823 - 103823

Опубликована: Май 30, 2024

Язык: Английский

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Predicting the subcutaneous temperature in cryolipolysis using deep operator networks

Thermal Science and Engineering Progress, Год журнала: 2024, Номер 55, С. 102946 - 102946

Опубликована: Сен. 30, 2024

Язык: Английский

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