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

What is it about?

Solving multidisciplinary optimization problems can be computationally intensive and time-consuming. A computational framework is presented to solve realistic problems and highlight its potential for reducing solution time. It relies on projection-based reduced-order models (PROMs) and on a new concept of active manifold (AM) to mitigate the curse of dimensionality during the training of PROMs. The method of AM relies on the nonlinear concept of a deep convolutional autoencoder for dimensionality reduction and it is proposed as a superior alternative to the concept of an active subspace whose capabilities are limited by an affine approximation.

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