Our source code for our work is available here.

Our environment is riddled with sensory stimuli that are noisy, ambiguous, and often incomplete, necessitating organisms to handle uncertainty in their sensory observations.

While existing approaches only model finite, discrete fidelities, in practice, the feasible fidelity choice is often infinite, which can correspond to a continuous mesh spacing orfinite element length.

Modern deep neural networks areoverparameterizedwith respect to the amount of training data and achieve zero training error, yet generalize well on test data.