Leveraging the Stochastic Predictions of Bayesian Neural Networks for Fluid Simulations

We investigate uncertainty estimation and multimodality via the non-deterministic predictions of Bayesian neural networks (BNNs) in fluid simulations. To this end, we deploy BNNs in two challenging experimental test-cases: We show that BNNs, when used as surrogate models for steady-state fluid flow predictions, provide accurate physical predictions together with sensible estimates of uncertainty.In our main experiment, we study BNNs in the context of differentiable solver interactions with turbulent plasma flows. We find that BNN-based corrector networks can stabilize coarse-grained simulations and successfully create diverse trajectories.