A comprehensive review of advances in physics-informed neural networks and their applications in complex fluid dynamics
C Zhao, F Zhang, W Lou, X Wang, J Yang - Physics of Fluids, 2024 - pubs.aip.org
Physics-informed neural networks (PINNs) represent an emerging computational paradigm
that incorporates observed data patterns and the fundamental physical laws of a given …
that incorporates observed data patterns and the fundamental physical laws of a given …
Solving partial differential equations using large-data models: a literature review
AM Hafiz, I Faiq, M Hassaballah - Artificial Intelligence Review, 2024 - Springer
Abstract Mathematics lies at the heart of engineering science and is very important for
capturing and modeling of diverse processes. These processes may be naturally-occurring …
capturing and modeling of diverse processes. These processes may be naturally-occurring …
Predictive modeling through physics‐informed neural networks for analyzing the thermal distribution in the partially wetted wavy fin
K Karthik, G Sowmya, N Sharma… - ZAMM‐Journal of …, 2024 - Wiley Online Library
The heat transport analysis and thermal distribution in partially wetted wavy profiled fin are
investigated in the current study. Convective, radiative effects and temperature‐dependent …
investigated in the current study. Convective, radiative effects and temperature‐dependent …
Deep mixed residual method for solving PDE-constrained optimization problems
J Yong, X Luo, S Sun, C Ye - Computers & Mathematics with Applications, 2024 - Elsevier
The deep mixed residual method (DeepMRM) is a technique to solve partial differential
equation. In this paper, it is applied to tackle PDE-constrained optimization problems (PDE …
equation. In this paper, it is applied to tackle PDE-constrained optimization problems (PDE …