Data-driven prediction of multistable systems from sparse measurements

DOI10.1063/5.0046203zbMath1475.37095arXiv2010.14706OpenAlexW3097930091MaRDI QIDQ5000863

Bryan Chu, Mohammad Farazmand

Publication date: 15 July 2021

Published in: Chaos: An Interdisciplinary Journal of Nonlinear Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2010.14706

zbMATH Keywords

reaction-diffusion equationFitzHugh-Nagumo modeldata-driven methodmultistable systems

Mathematics Subject Classification ID

Reaction-diffusion equations (35K57) Approximation methods and numerical treatment of dynamical systems (37M99)

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Uses Software

• LMNN

Cites Work

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• Existence and stability for Fokker-Planck equations with log-concave reference measure
• A family of embedded Runge-Kutta formulae
• Reduced-order prediction of rogue waves in two-dimensional deep-water waves
• Diffusion maps, spectral clustering and reaction coordinates of dynamical systems
• THE GEOMETRY OF DISSIPATIVE EVOLUTION EQUATIONS: THE POROUS MEDIUM EQUATION
• Sparse Sensor Placement Optimization for Classification
• Compressive sensing based machine learning strategy for characterizing the flow around a cylinder with limited pressure measurements
• Tipping elements in the Earth's climate system
• Uncertainty Quantification in Graph-Based Classification of High Dimensional Data
• Fast Solution of $\ell _{1}$-Norm Minimization Problems When the Solution May Be Sparse
• Weighted Sobolev spaces and embedding theorems
• The Variational Formulation of the Fokker--Planck Equation
• Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps
• Particle Image Velocimetry for Complex and Turbulent Flows
• Data-Driven Sparse Sensor Placement for Reconstruction: Demonstrating the Benefits of Exploiting Known Patterns
• Mitigation of tipping point transitions by time-delay feedback control
• Data Assimilation
• Sparse Sensing and DMD-Based Identification of Flow Regimes and Bifurcations in Complex Flows
• Regularization and Variable Selection Via the Elastic Net
• Pattern formation outside of equilibrium