ATLAS: A Geometric Approach to Learning High-Dimensional Stochastic Systems Near Manifolds

DOI10.1137/140970951zbMath1362.60066arXiv1404.0667OpenAlexW2964057431MaRDI QIDQ5737746

Miles M. Crosskey, Mauro Maggioni

Publication date: 30 May 2017

Published in: Multiscale Modeling & Simulation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1404.0667

zbMATH Keywords

dynamical systemslearninghomogenizationmodel reductiondimensionality reductionstochastic different equations

Mathematics Subject Classification ID

Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20) Diffusion processes (60J60) Transition functions, generators and resolvents (60J35)

Related Items

Learning interaction kernels in stochastic systems of interacting particles from multiple trajectories, Computing the quasipotential for nongradient SDEs in 3D, Nonlinear model reduction for slow-fast stochastic systems near unknown invariant manifolds, Optimal control for sampling the transition path process and estimating rates, Transition manifolds of complex metastable systems. Theory and data-driven computation of effective dynamics, Physics-Constrained, Data-Driven Discovery of Coarse-Grained Dynamics, Discovering mean residence time and escape probability from data of stochastic dynamical systems, Data-driven spectral decomposition and forecasting of ergodic dynamical systems

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