4 papers
Multi-Objective Loss Balancing in Physics-Informed Neural Networks for Fluid Flow Applications
Afrah Farea, Saiful Khan, Mustafa Serdar Celebi
Physics-Informed Neural Networks (PINNs) have emerged as a promising machine learning approach for solving partial differential equations (PDEs). However, PINNs face significant ch…
Learning Fluid-Structure Interaction with Physics-Informed Machine Learning and Immersed Boundary Methods
Afrah Farea, Saiful Khan, Reza Daryani +2
Physics-informed neural networks (PINNs) have emerged as a promising approach for solving complex fluid dynamics problems, yet their application to fluid-structure interaction (FSI…
QCPINN: Quantum-Classical Physics-Informed Neural Networks for Solving PDEs
Afrah Farea, Saiful Khan, Mustafa Serdar Celebi
Physics-informed neural networks (PINNs) have emerged as promising methods for solving partial differential equations (PDEs) by embedding physical laws within neural architectures.…
Learnable Activation Functions in Physics-Informed Neural Networks for Solving Partial Differential Equations
Afrah Farea, Mustafa Serdar Celebi
Physics-Informed Neural Networks (PINNs) have emerged as a promising approach for solving Partial Differential Equations (PDEs). However, they face challenges related to spectral b…