Assessing Model Uncertainties Through Proper Experimental Design

Hvidevold, Hilde Kristine; Alendal, Guttorm; Johannessen, Truls; Mannseth, Trond

doi:10.1016/j.egypro.2013.06.233

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2021

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“…43,44 Subsequently, these transport predictions support design of marine environmental monitoring programs. [45][46][47][48] The aim here is to model current conditions in a probabilistic manner using SCVAEs. This allows for predicting footprints in a Monte Carlo framework, providing simulated data for training networks used for analyzing environmental time series.…”

Section: Introductionmentioning

confidence: 99%

Semi-conditional variational auto-encoder for flow reconstruction and uncertainty quantification from limited observations

et al. 2021

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We present a new data-driven model to reconstruct nonlinear flow from spatially sparse observations. The proposed model is a version of a Conditional Variational Auto-Encoder (CVAE), which allows for probabilistic reconstruction and thus uncertainty quantification of the prediction. We show that in our model, conditioning on measurements from the complete flow data leads to a CVAE where only the decoder depends on the measurements. For this reason, we call the model semi-conditional variational autoencoder. The method, reconstructions, and associated uncertainty estimates are illustrated on the velocity data from simulations of 2D flow around a cylinder and bottom currents from a simulation of the southern North Sea by the Bergen Ocean Model. The reconstruction errors are compared to those of the Gappy proper orthogonal decomposition method.

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