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A Nonlinear Physics-based Reduced Order Model with Convolutional-based Operator Compression
Reduced-order models (ROMs) are widely used to accelerate the solution of parametrized partial differential equations, with projection-based methods relying on a linear representation of the governing equations in a low-dimensional space. Their efficiency, however, is fundamentally limited by the Ko
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