CD Technical Meeting (ML9): Particle Filters for Uncertainty Propagation in PDE Models
Machine Learning, Uncertainty Quantification and Data Science
Propagating uncertainty is a key part of modelling systems governed by Partial Differential Equations (PDEs). In many applications, including plasma physics, quantifying uncertainty is important for reliable prediction and risk assessment. We present the use of a Particle Filter for a Bayesian approach to state estimation first demonstrated with a simple heat equation example. We then outline an extension of this method for TORAX, a differentiable plasma transport code that solves a set of non-linear coupled PDEs. We describe how particle filters can be combined with GPU-accelerated solvers to enable a method of uncertainty propagation in complex physics models.