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A Deep Learning approach to Reduced Order Modelling of Parameter Dependent Partial Differential Equations
Our work is based on the use of deep autoencoders, which we employ for encoding and decoding a high fidelity approximation of the solution manifold.
Learning High-Order Interactions via Targeted Pattern Search
The algorithm relies on an interaction learning step based on a well-known frequent item set mining algorithm, and a novel dissimilarity-based interaction selection step that allows the user to specify the number of interactions to be included in the LR model.
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