Difference between revisions of "GP SSM"

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(Papers which are more oriented toward control)
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* D. Giannakis, [[Media:DataDrivenSpectralDecomposition.pdf | Data-Driven Spectral Decomposition and Forecasting of ergodic dynamical systems]], ''arXiv:1507.02338v2''
 
* D. Giannakis, [[Media:DataDrivenSpectralDecomposition.pdf | Data-Driven Spectral Decomposition and Forecasting of ergodic dynamical systems]], ''arXiv:1507.02338v2''
 
* I. Mezic and A. Surana, [[Media:PeriodicKoopmanModeDecomposition.pdf | Koopman Mode Decomposition for Periodic/Quasi-Periodic Time Dependence]], ''IFAC Papers Online, 48-18, pp. 690697, 2016.
 
* I. Mezic and A. Surana, [[Media:PeriodicKoopmanModeDecomposition.pdf | Koopman Mode Decomposition for Periodic/Quasi-Periodic Time Dependence]], ''IFAC Papers Online, 48-18, pp. 690697, 2016.
 +
* H. Schaeffer, [[Media:LearningPDEs.pdf | Learning Partial Differential Equations via Data Discovery and Sparse Optimization]], ''J. Royal Society, Proceedings A'', 2017
  
 
==== Papers which are more oriented toward control ====
 
==== Papers which are more oriented toward control ====

Revision as of 01:19, 9 January 2018

This page gathers references and materials related to the study of

  • Gaussian Process (GP) State Space Models (SSM)
  • Deep Learning
  • Koopman Spectral Methods.

Gaussian Process Approaches

Basic Gaussian Process Info

  • Rasmussen and Williams

Web Links

Papers on GP-SSMs


Deep Learning

Papers on Deep Learning

  • Soatto Paper

Web Links

Koopman Spectral Method

Papers on Koopman Spectral methods

Papers which are more oriented toward control

Papers which are more oriented toward fluids

Some Early Papers

Other Papers