Frank Schäfer

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

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Sessions

3min
MitosisStochasticDiffEq.jl - Filtering & Guiding for SDEs
Frank Schäfer

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.