Probabilistic model-error assessment of deep learning proxies: an application to real-time inversion of borehole electromagnetic measurements. (arXiv:2205.12684v1 [physics.geo-ph])

The advent of fast sensing technologies allows for real-time model updates in
many applications where the model parameters are uncertain. Bayesian
algorithms, such as ensemble smoothers, offer a real-time probabilistic
inversion accounting for uncertainties. However, they rely on the repeated
evaluation of the computational models, and deep neural network (DNN) based
proxies can be useful to address this computational bottleneck. This paper
studies the effects of the approximate nature of the deep learned models and
associated model errors during the …

application arxiv deep learning error learning physics proxies real-time time