The application of physics-informed machine learning in multiphysics modeling in chemical engineering
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 …
new approach to tackle multiphysics modeling problems prevalent in the field of chemical …
Machine learning-based surrogate modeling approaches for fixed-wing store separation
In pursuit of deriving a limited expense store trajectory prediction model, this work
investigates the application of two data-driven surrogate modeling approaches for the …
investigates the application of two data-driven surrogate modeling approaches for the …
Machine learning-based reduced-order reconstruction method for flow fields
H Gao, W Qian, J Dong, J Liu - Energy and Buildings, 2024 - Elsevier
The real-time prediction of flow fields has scientific and engineering significance, although it
is currently challenging. To address this issue, we propose a nonintrusive supervised …
is currently challenging. To address this issue, we propose a nonintrusive supervised …
Rapid prediction of indoor airflow field using operator neural network with small dataset
H Gao, W Qian, J Dong, J Liu - Building and Environment, 2024 - Elsevier
Indoor airflow is one of the most critical factors affecting room comfort. The accurate
prediction of indoor airflow fields is essential for efficient environmental control. However …
prediction of indoor airflow fields is essential for efficient environmental control. However …
Physics-informed neural networks for parametric compressible Euler equations
S Wassing, S Langer, P Bekemeyer - Computers & Fluids, 2024 - Elsevier
The numerical approximation of solutions to the compressible Euler and Navier–Stokes
equations is a crucial but challenging task with relevance in various fields of science and …
equations is a crucial but challenging task with relevance in various fields of science and …
[HTML][HTML] Pressure swing adsorption process modeling using physics-informed machine learning with transfer learning and labeled data
Pressure swing adsorption (PSA) modeling remains a challenging task since it exhibits
strong dynamic and cyclic behavior. This study presents a systematic physics-informed …
strong dynamic and cyclic behavior. This study presents a systematic physics-informed …
[HTML][HTML] Evaluation of supervised machine learning regression models for CFD-based surrogate modelling in indoor airflow field reconstruction
Fast and reliable prediction of indoor airflow distribution is critical for indoor environment
control. While neural networks (NN), often interchangeably referred to as Back Propagation …
control. While neural networks (NN), often interchangeably referred to as Back Propagation …
Transfer learning and pretraining enhanced physics-informed machine learning for closed-circuit reverse osmosis modeling
Closed-circuit reverse osmosis (CCRO) is a widely concerned batch-type desalination
process that exhibits dynamic, multi-mode, and cyclic behavior. This study provides a novel …
process that exhibits dynamic, multi-mode, and cyclic behavior. This study provides a novel …
Reconstruction of downburst wind fields using physics-informed neural network
B Yao, Z Wang, Z Fang, Z Li - Journal of Wind Engineering and Industrial …, 2024 - Elsevier
Downbursts, as a strong localized wind event, have caused significant damage to
engineering structures throughout the world. However, given the spatial and temporal …
engineering structures throughout the world. However, given the spatial and temporal …
A fast three-dimensional flow field prediction around bluff bodies using deep learning
F Nemati Taher, A Subaşı - Physics of Fluids, 2024 - pubs.aip.org
This study presents a deep learning approach for predicting the flow field in the
incompressible turbulent three-dimensional (3D) external flow around right-rhombic prism …
incompressible turbulent three-dimensional (3D) external flow around right-rhombic prism …