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Multi-Objective Loss Balancing in Physics-Informed Neural Networks for Fluid Flow Applications
Physics-Informed Neural Networks (PINNs) have emerged as a promising machine learning approach for solving partial differential equations (PDEs). However, PINNs face significant challenges in balancing multi-objective losses, as multiple competing loss terms such as physics residuals, boundary condi
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