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PINNs

model1 events
papersTODAY 04:00 UTC

Conflict-Free Gradients Target Failure Modes in PINNs and PIKANs

A new arXiv preprint examines why physics-informed neural networks (PINNs) and their Kolmogorov-Arnold counterparts (PIKANs) often fail when used with domain decomposition to solve partial differential equations over complex geometries. The authors attribute these problems to conflicting gradient signals and propose a conflict-free gradient approach to improve training stability and scalability.

research/pinns/scientific-ml/training