Neural Stochastic Flows: Solver-Free Modelling and Inference for SDE Solutions

arXiv — stat.MLThursday, October 30, 2025 at 4:00:00 AM
Researchers have introduced Neural Stochastic Flows (NSFs), a groundbreaking approach to modeling stochastic differential equations (SDEs) without the need for traditional numerical solvers. This innovation is significant as it allows for more efficient handling of noisy and irregular time series data commonly found in fields like finance, physics, and machine learning. By leveraging conditional normalizing flows, NSFs can learn SDE transition laws directly, potentially transforming how we analyze complex data.
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