Boumediene Hamzi | DeepAI

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∙ 09/24/2022

One-Shot Learning of Stochastic Differential Equations with Computational Graph Completion

We consider the problem of learning Stochastic Differential Equations of...

10 Matthieu Darcy, et al. ∙

∙ 11/25/2021

Learning dynamical systems from data: A simple cross-validation perspective, part III: Irregularly-Sampled Time Series

A simple and interpretable way to learn a dynamical system from data is ...

0 Jonghyeon Lee, et al. ∙

∙ 12/01/2020

Kernel methods for center manifold approximation and a data-based version of the Center Manifold Theorem

For dynamical systems with a non hyperbolic equilibrium, it is possible ...

0 Bernard Haasdonk, et al. ∙

∙ 07/09/2020

Learning dynamical systems from data: a simple cross-validation perspective

Regressing the vector field of a dynamical system from a finite number o...

16 Boumediene Hamzi, et al. ∙

∙ 05/27/2020

Kernel-based approximation of the Koopman generator and Schrödinger operator

Many dimensionality and model reduction techniques rely on estimating do...

6 Stefan Klus, et al. ∙

∙ 04/18/2019

A kernel-based method for coarse graining complex dynamical systems

We present a novel kernel-based machine learning algorithm for identifyi...

0 Andreas Bittracher, et al. ∙

∙ 04/25/2018

A Note on Kernel Methods for Multiscale Systems with Critical Transitions

We study the maximum mean discrepancy (MMD) in the context of critical t...

0 Boumediene Hamzi, et al. ∙

∙ 04/03/2012

Kernel Methods for the Approximation of Some Key Quantities of Nonlinear Systems

We introduce a data-based approach to estimating key quantities which ar...

0 Jake Bouvrie, et al. ∙

∙ 08/14/2011

Kernel Methods for the Approximation of Nonlinear Systems

We introduce a data-driven order reduction method for nonlinear control ...

0 Jake Bouvrie, et al. ∙

∙ 11/12/2010

Balanced Reduction of Nonlinear Control Systems in Reproducing Kernel Hilbert Space

We introduce a novel data-driven order reduction method for nonlinear co...

0 Jake Bouvrie, et al. ∙

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