Kernel-based parameter estimation of dynamical systems with unknown observation functions
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Publication:4989104
DOI10.1063/5.0044529zbMath1460.37075arXiv2009.04142OpenAlexW3153933809MaRDI QIDQ4989104
Gal Mishne, Ofir Lindenbaum, Ronen Talmon, Amir Sagiv
Publication date: 20 May 2021
Published in: Chaos: An Interdisciplinary Journal of Nonlinear Science (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2009.04142
Cites Work
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- Discovering governing equations from data by sparse identification of nonlinear dynamical systems
- A data-driven approximation of the koopman operator: extending dynamic mode decomposition
- Kernel methods in machine learning
- Learning the geometry of common latent variables using alternating-diffusion
- Parsimonious representation of nonlinear dynamical systems through manifold learning: a chemotaxis case study
- Poincaré maps for multiscale physics discovery and nonlinear Floquet theory
- Data-driven predictions of the Lorenz system
- Calibrate, emulate, sample
- Multi-view kernel consensus for data analysis
- Gaussian bandwidth selection for manifold learning and classification
- Manifold approximation by moving least-squares projection (MMLS)
- Kernel flows: from learning kernels from data into the abyss
- Manifold learning for parameter reduction
- Observability and structural identifiability of nonlinear biological systems
- An interior algorithm for nonlinear optimization that combines line search and trust region steps
- Diffusion maps
- Inverse problems: A Bayesian perspective
- 10.1162/153244303768966085
- Experimental Design for Nonparametric Correction of Misspecified Dynamical Models
- Data-Driven Reduction for a Class of Multiscale Fast-Slow Stochastic Dynamical Systems
- A First Course in the Numerical Analysis of Differential Equations
- Automated reverse engineering of nonlinear dynamical systems
- Deterministic Nonperiodic Flow
- Reconstruction of normal forms by learning informed observation geometries from data
- An Interior Point Algorithm for Large-Scale Nonlinear Programming
- Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
- Discovery of Dynamics Using Linear Multistep Methods
- Data-driven discovery of coordinates and governing equations
- Algorithmic Learning Theory
- Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data
- A trust region method based on interior point techniques for nonlinear programming.
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