Active Manifold and Model Order Reduction to Accelerate Multidisciplinary Analysis and Optimization - presented by Prof. Charbel Farhat

Active Manifold and Model Order Reduction to Accelerate Multidisciplinary Analysis and Optimization

Prof. Charbel Farhat

Prof. Charbel Farhat
Slide at 52:41
Charbel Construction of Nonlinear Function gu and Associated Active Manifold Augment the training data as follows Sk = U{Sk) Construct g u using an autoencoder - that is, a forward artificial neural network Encoder function Decoder function Scaling Inverse operator Scaling operator Latent features Input Reconstructed vector vector Convolutional layers Fully-connected layers Transpose convolutional layers
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Summary (AI generated)

When you give data to an autoencoder, it will detect all the features contained within, focusing on the essential information. This process is similar to a nonlinear version of a singular value decomposition. The autoencoder provides a mapping that allows for the transition between a lower-dimensional parameter space and a higher-dimensional one.

For a quick example, let's move on to a multiobjective scenario.