Reconstruction of normal forms by learning informed observation geometries from data
From MaRDI portal
Publication:4646127
DOI10.1073/pnas.1620045114zbMath1407.62306arXiv1612.03195OpenAlexW2745846861WikidataQ38613371 ScholiaQ38613371MaRDI QIDQ4646127
Or Yair, Ronen Talmon, Ioannis G. Kevrekidis, Ronald R. Coifman
Publication date: 11 January 2019
Published in: Proceedings of the National Academy of Sciences (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1612.03195
Related Items (11)
Numerical bifurcation analysis of PDEs from lattice Boltzmann model simulations: a parsimonious machine learning approach ⋮ Parsimony as the ultimate regularizer for physics-informed machine learning ⋮ Data-driven reduced order models using invariant foliations, manifolds and autoencoders ⋮ Identifying stochastic governing equations from data of the most probable transition trajectories ⋮ Detecting the maximum likelihood transition path from data of stochastic dynamical systems ⋮ Manifold learning for organizing unstructured sets of process observations ⋮ SINDy-PI: a robust algorithm for parallel implicit sparse identification of nonlinear dynamics ⋮ Model selection for hybrid dynamical systems via sparse regression ⋮ Kernel-based parameter estimation of dynamical systems with unknown observation functions ⋮ Spatiotemporal pattern extraction by spectral analysis of vector-valued observables ⋮ Unnamed Item
This page was built for publication: Reconstruction of normal forms by learning informed observation geometries from data
