PhD candidate in physics at the University of Basel.
Member of the SciML open source software organization for scientific machine learning.
Google Summer of Code 2020 student with the project: High weak order SDE solvers and their utility in neural SDEs. Google Summer of Code 2021 student with the project: Neural Hybrid Differential Equations and Adjoint Sensitivity Analysis.
Github & Slack: @frankschae
Stochastic differential equations (SDEs) arise naturally in many scientific and industrial disciplines, e.g., due to the interaction of a system with some environment. Inverse problems such as the inference of the model parameters of an SDE are of paramount importance. I describe the Backward Filtering Forward Guiding paradigm, capable of solving this task based on trajectories observed according to some observation scheme, suitable for neural SDEs, and scalable to high-dimensional systems.