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Tackling Failure Modes of PINNs and PIKANs Using Conflict-Free Gradients
Scientific machine learning methods such as physics-informed neural networks (PINNs) increasingly rely on domain decomposition for better scalability while solving partial differential equations (PDEs) over complex geometries, yet the resulting composite loss comprising residual, boundary, and inter
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