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Found 9 papers, 1 papers with code
Neural State-Dependent Delay Differential Equations
The developed framework is auto-differentiable and runs efficiently on multiple backends.
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.
Curriculum learning for data-driven modeling of dynamical systems
Based on that, we consider entropy as a metric of complexity of the dataset; we show how an informed design of the training set based on the analysis of the entropy significantly improves the resulting models in terms of generalizability, and provide insights on the amount and the choice of data required for an effective data-driven modeling.
Shallow Neural Networks for Fluid Flow Reconstruction with Limited Sensors
In many applications, it is important to reconstruct a fluid flow field, or some other high-dimensional state, from limited measurements and limited data.
Diffusion Maps meet Nyström
Diffusion maps are an emerging data-driven technique for non-linear dimensionality reduction, which are especially useful for the analysis of coherent structures and nonlinear embeddings of dynamical systems.
Observable dictionary learning for high-dimensional statistical inference
The quantity of interest (QoI) is approximated in a basis (dictionary) learned from a training set.
A statistical learning strategy for closed-loop control of fluid flows
This work discusses a closed-loop control strategy for complex systems utilizing scarce and streaming data.
