  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 three challenging experimental
test-cases of increasing complexity: 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. Further, we
experiment with perturbed temporal sequences from Navier-Stokes simulations and
evaluate the capabilities of BNNs to capture multimodal evolutions. While our
findings indicate that this is problematic for large perturbations, our results
show that the networks learn to correctly predict high uncertainties in such
situations. Finally, we study BNNs in the context of solver interactions with
turbulent plasma flows. We find that BNN-based corrector networks can stabilize
coarse-grained simulations and successfully create multimodal trajectories.
