A machine learning framework for uncovering stochastic nonlinear dynamics from noisy data

Researchers present a hybrid framework combining deep symbolic regression with Gaussian processes to recover governing equations from noisy real-world data while quantifying parameter uncertainty. The approach bridges symbolic and probabilistic methods without requiring prior functional form assumptions, with applications to financial markets, biological systems, and ecosystems.