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In Situ Monitoring and Steering Deep Learning Training from Numerical Simulations in ParaView-Catalyst
DescriptionIn the context of numerical simulation, a surrogate model approximates the outputs of a solver with a low computational cost. In this article, we present an In Situ visualization prototype, based on Catalyst 2, for monitoring the training of surrogate models based on Deep Neural Networks. We believe that In Situ monitoring can help solve a fundamental problem of this kind of training: standard metrics, such as the Mean Squared Error, do not convey enough information on which simulation aspects are harder to learn. Our prototype allows the interactive visualization of the current state of convergence of a physical quantity spatial field, complementing the traditional loss function value curve. We enable the steering of physical parameters during the training process, for interactive exploration. We also allow the user to influence the learning process in real-time by changing the learning rate. Results are illustrated on a Computational Fluids Dynamics use case.
In Situ Processing