Public articles linked to the same research event.
arXiv The authors propose Higher-Order-KANDy, which combines delay and derivative (jet) embeddings with derivative-ordered KANDy layers stacked into a machine learning architecture and learns a delay-to-jet map as a Kolmogorov-Arnold network, handling higher-order systems natively without a candidate library; on synthetic benchmarks it recovers equations under coarse sampling and noise, and at noise levels up to 7.5% it selects the exact active set on every seed, whereas an oracle weak-form regression admits a spurious term at that noise level, though the oracle remains the more accurate coefficient estimator.
The authors propose Higher-Order-KANDy, which combines delay and derivative (jet) embeddings with derivative-ordered KANDy layers stacked into a machine learning architecture and learns a delay-to-jet map as a Kolmogorov-Arnold network, handling higher-order systems natively without a candidate library; on synthetic benchmarks it recovers equations under coarse sampling and noise, and at noise levels up to 7.5% it selects the exact active set on every seed, whereas an oracle weak-form regression admits a spurious term at that noise level, though the oracle remains the more accurate coefficient estimator.
The authors propose Higher-Order-KANDy, which combines delay and derivative (jet) embeddings with derivative-ordered KANDy layers stacked into a machine learning architecture and learns a delay-to-jet map as a Kolmogorov-Arnold network, handling higher-order systems natively without a candidate library; on synthetic benchmarks it recovers equations under coarse sampling and noise, and at noise levels up to 7.5% it selects the exact active set on every seed, whereas an oracle weak-form regression admits a spurious term at that noise level, though the oracle remains the more accurate coefficient estimator.
The authors propose Higher-Order-KANDy, which combines delay and derivative (jet) embeddings with derivative-ordered KANDy layers stacked into a machine learning architecture and learns a delay-to-jet map as a Kolmogorov-Arnold network, handling higher-order systems natively without a candidate library; on synthetic benchmarks it recovers equations under coarse sampling and noise, and at noise levels up to 7.5% it selects the exact active set on every seed, whereas an oracle weak-form regression admits a spurious term at that noise level, though the oracle remains the more accurate coefficient estimator.