In this tutorial we demonstrate how one can use TKRISK to analyze vaccine response to COVID19. We create a simple model of contamination and show how vaccine and exposition can jointly affect the probability of being infected.
We review with this example the logic and applicability of Bayesian networks. We are able to closely match the analytical solution thanks to efficient sampling methods including low discrepancy sequences. This is the first of a three parts demo.
In this tutorial we demonstrate how one can use TKRISK to analyze vaccine response to COVID19. We create a simple model of contamination and show how vaccine and exposition can jointly affect the probability of being infected.
We review with this example the logic and applicability of Bayesian networks. We are able to closely match the analytical solution thanks to efficient sampling methods including low discrepancy sequences. This is the first of a three parts demo.
PHYSICS INFORMED
SOLUTIONS
Surrogate Reservoir Models
Fast and efficient way to generate high fidelity surrogate responses to the various realizations of a simulation model. The model is built using a convolutional network encoder decoder architecture that takes the reservoir permeability as an input and outputs the pressure and saturation fields at various time steps.
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