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arXiv Machine Learning By Arthur Bizzi, Olga Fink

Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise

Read the original on arXiv Machine Learning →

arXiv:2607. 19173v1 Announce Type: new Abstract: Neural stochastic differential equations (SDEs) have emerged as powerful tools for learning noisy or stochastic dynamics directly from data; however, existing approaches largely assume uncoupled and continuous noise, limiting their applicability to realistic stochastic drivers, and often scale poorly in time, requiring expensive autoregressive training.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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