AISTATS2022: Probabilistic Numerical Method of Lines for Time-Dependent PDEs
Probabilistic Numerical Method of Lines for Time-Dependent Partial Differential Equations
Nicholas Krämer, Jonathan Schmidt, Philipp Hennig
Artificial Intelligence and Statistics (AISTATS) 2022
► Paper: https://arxiv.org/abs/2110.11847
► Code: https://github.com/schmidtjonathan/pnmol-experiments
This work develops a class of probabilistic algorithms for the numerical solution of nonlinear, time-dependent partial differential equations (PDEs). Current state-of-the-art PDE solvers treat the space- and time-dimensions separately, serially, and with black-box algorithms, which obscures the interactions between spatial and temporal approximation errors and misguides the quantification of the overall error. To fix this issue, we introduce a probabilistic version of a technique called method of lines. The proposed algorithm begins with a Gaussian process interpretation of finite difference methods, which then interacts naturally with filtering-based probabilistic ordinary differential equation (ODE) solvers because they share a common language: Bayesian inference. Joint quantification of space- and time-uncertainty becomes possible without losing the performance benefits of well-tuned ODE solvers. Thereby, we extend the toolbox of probabilistic programs for differential equation simulation to PDEs.
► Find out more about our research at https://uni-tuebingen.de/en/160189