ISALT: inference-based schemes adaptive to large time-stepping for locally Lipschitz ergodic systems
DOI10.3934/dcdss.2021103zbMath1484.65015arXiv2102.12669OpenAlexW3204582391WikidataQ114022625 ScholiaQ114022625MaRDI QIDQ2129142
Publication date: 22 April 2022
Published in: Discrete and Continuous Dynamical Systems. Series S (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2102.12669
stochastic differential equationsdata-driven modelinginference-based schemelocally Lipschitz ergodic systemsmodel reduction in time
Inference from stochastic processes and prediction (62M20) Computational methods for stochastic equations (aspects of stochastic analysis) (60H35) Numerical solutions to stochastic differential and integral equations (65C30) Computational methods for ergodic theory (approximation of invariant measures, computation of Lyapunov exponents, entropy, etc.) (37M25)
Related Items (1)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Sampling, feasibility, and priors in data assimilation
- Strong convergence of an explicit numerical method for SDEs with nonglobally Lipschitz continuous coefficients
- Stochastic stability of differential equations. With contributions by G. N. Milstein and M. B. Nevelson
- A parareal in time procedure for the control of partial differential equations
- Modeling of missing dynamical systems: deriving parametric models using a nonparametric framework
- Taylor expansions of solutions of stochastic partial differential equations with additive noise
- Exponential convergence of Langevin distributions and their discrete approximations
- Data-based stochastic model reduction for the Kuramoto-Sivashinsky equation
- DGM: a deep learning algorithm for solving partial differential equations
- A data-driven approach for discovering stochastic dynamical systems with non-Gaussian Lévy noise
- Data-driven model reduction, Wiener projections, and the Koopman-Mori-Zwanzig formalism
- Molecular dynamics. With deterministic and stochastic numerical methods
- Parameter estimation for multiscale diffusions
- Ergodicity for SDEs and approximations: locally Lipschitz vector fields and degenerate noise.
- Numerical approximations of stochastic differential equations with non-globally Lipschitz continuous coefficients
- Numerical Treatment of Stochastic Differential Equations
- On the central limit theorem for stationary processes
- Data-driven parameterization of the generalized Langevin equation
- Physics constrained nonlinear regression models for time series
- Effective dynamics using conditional expectations
- Solving high-dimensional partial differential equations using deep learning
- Coarse-Graining of Overdamped Langevin Dynamics via the Mori--Zwanzig Formalism
- Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations
- Learning data-driven discretizations for partial differential equations
- Nonparametric inference of interaction laws in systems of agents from trajectory data
- Effective dynamics for non-reversible stochastic differential equations: a quantitative study
- Data Assimilation
- Coarse-grained stochastic models for tropical convection and climate
This page was built for publication: ISALT: inference-based schemes adaptive to large time-stepping for locally Lipschitz ergodic systems