CausticFlow: An Efficient Machine Learning Framework Combining Neural Differential Equations and Normalizing Flows for Binary Microlensing Parameter Inference

We introduce CausticFlow, a machine learning framework that combines neural controlled differential equations with normalizing flows to infer binary microlensing parameters. This architecture naturally handles irregularly sampled time series and data gaps while flexibly capturing strongly correlated