In-Context Learning of Stochastic Differential Equations with Foundation Inference Models
Researchers have developed a suite of Foundation Inference Models (FIMs) designed to rapidly estimate parameters for various differential equations from time-series data. These models, including FIM-SDE for stochastic differential equations, FIM-PP for temporal point processes, and FIM-ODE for ordinary differential equations, are pretrained on broad distributions of synthetic data. This pretraining allows them to perform in-context (zero-shot) inference or be quickly fine-tuned to specific datasets, often outperforming traditional methods and specialized models that require extensive training. AI
IMPACT These foundation models could significantly speed up scientific discovery by enabling faster and more accurate parameter estimation for complex dynamical systems.