[HTML][HTML] Semi-conditional variational auto-encoder for flow reconstruction and uncertainty quantification from limited observations
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 …
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 …
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