GeoKAN learns a diagonal Riemannian metric to warp inputs for KAN models, enabling task-dependent resolution allocation for sharp and non-uniform regimes.
An expert’s guide to training physics-informed neural networks
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PINNACLE is an open-source framework for classical and quantum PINNs that supplies modular training methods and benchmarks showing high sensitivity to architecture choices plus parameter-efficiency gains in some hybrid quantum regimes.
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Geometric Kolmogorov--Arnold Network (GeoKAN)
GeoKAN learns a diagonal Riemannian metric to warp inputs for KAN models, enabling task-dependent resolution allocation for sharp and non-uniform regimes.
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PINNACLE: An Open-Source Computational Framework for Classical and Quantum PINNs
PINNACLE is an open-source framework for classical and quantum PINNs that supplies modular training methods and benchmarks showing high sensitivity to architecture choices plus parameter-efficiency gains in some hybrid quantum regimes.