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Found 4 papers, 0 papers with code
Interpretable learning of effective dynamics for multiscale systems
In recent years, techniques based on deep recurrent neural networks have produced promising results for the modeling and simulation of complex spatiotemporal systems and offer large flexibility in model development as they can incorporate experimental and computational data.
Continuous Methods : Adaptively intrusive reduced order model closure
Reduced order modeling methods are often used as a mean to reduce simulation costs in industrial applications.
Continuous Methods : Hamiltonian Domain Translation
This paper proposes a novel approach to domain translation.
CD-ROM: Complemented Deep-Reduced Order Model
Model order reduction through the POD-Galerkin method can lead to dramatic gains in terms of computational efficiency in solving physical problems.
