Bayesian learning of stochastic dynamical models

DOI10.1016/j.physd.2021.133003zbMath1491.76052OpenAlexW3198406843MaRDI QIDQ2077593

Peter Lu, Pierre F. J. Lermusiaux

Publication date: 21 February 2022

Published in: Physica D (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.physd.2021.133003

zbMATH Keywords

learningdynamical systemBayesian data assimilationmicroorganism concentrationocean velocityviscous incompressible wake

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Stochastic analysis applied to problems in fluid mechanics (76M35) Wakes and jets (76D25) Geophysical flows (76U60)

Related Items (1)

Measurements-based constrained control optimization in presence of uncertainties with application to the driver commands for high-speed trains

Uses Software

• tsbridge
• DDDAS
• PDE-Net

Cites Work

• Discovering governing equations from data by sparse identification of nonlinear dynamical systems
• Towards an adaptive POD/SVD surrogate model for aeronautic design
• High-fidelity real-time simulation on deployed platforms
• Modeling and analyzing social network dynamics using stochastic discrete graphical dynamical systems
• A general science-based framework for dynamical spatio-temporal models
• Dynamical criteria for the evolution of the stochastic dimensionality in flows with uncertainty
• Dynamically orthogonal field equations for continuous stochastic dynamical systems
• Multiscale stochastic modeling for tractable inference and data assimilation
• Inductive process modeling
• Steady viscous flow past a circular cylinder up to Reynolds number 600
• Stochastic models, estimation, and control. Vol. 1
• An introduction to MCMC for machine learning
• Bayesian model averaging: A tutorial. (with comments and a rejoinder).
• Hidden physics models: machine learning of nonlinear partial differential equations
• Dynamic data-driven reduced-order models
• Projection-based model reduction: formulations for physics-based machine learning
• Steady non-Newtonian flow past a circular cylinder: a numerical study
• Computational science -- ICCS 2004. 4th international conference, Kraków, Poland, June 6--9, 2004. Proceedings, Part III.
• Bridging the gap: machine learning to resolve improperly modeled dynamics
• DLGA-PDE: discovery of PDEs with incomplete candidate library via combination of deep learning and genetic algorithm
• DeepMoD: deep learning for model discovery in noisy data
• PDE-Net 2.0: learning PDEs from data with a numeric-symbolic hybrid deep network
• Reduced models of atmospheric low-frequency variability: parameter estimation and comparative performance
• A Bayesian tutorial for data assimilation
• Graphical models for statistical inference and data assimilation
• Adaptive modeling, adaptive data assimilation and adaptive sampling
• POD and CVT-based reduced-order modeling of Navier-Stokes flows
• Stochastic processes and filtering theory
• Sequential Monte Carlo Methods in Practice
• Properties of nested sampling
• Partial synchronization in stochastic dynamical networks with switching communication channels
• Global analysis of Navier–Stokes and Boussinesq stochastic flows using dynamical orthogonality
• Laminar flow of an incompressible fluid past a bluff body: the separation, reattachment, eddy properties and drag
• Experiments on the flow past a circular cylinder at low Reynolds numbers
• Co-evolutionary algorithm for structural damage identification using minimal physical testing
• Automated reverse engineering of nonlinear dynamical systems
• A finite-element study of the onset of vortex shedding in flow past variously shaped bodies
• A numerical study of steady viscous flow past a circular cylinder
• A study of two-dimensional flow past an oscillating cylinder
• Flow past a rotating cylinder
• From spheres to circular cylinders: the stability and flow structures of bluff ring wakes
• Gaussian Multiresolution Models: Exploiting Sparse Markov and Covariance Structure
• Dynamically Orthogonal Numerical Schemes for Efficient Stochastic Advection and Lagrangian Transport
• Model Reduction and Approximation
• LII. An essay towards solving a problem in the doctrine of chances. By the late Rev. Mr. Bayes, F. R. S. communicated by Mr. Price, in a letter to John Canton, A. M. F. R. S
• Bayes Factors
• A Geometric Approach to Dynamical Model Order Reduction
• Data-Driven Identification of Parametric Partial Differential Equations
• Hierarchical Spatiotemporal Matrix Models for Characterizing Invasions
• Hydrodynamics Around Cylindrical Structures
• Numerical solutions for steady flow past a circular cylinder at Reynolds numbers up to 100
• Data Assimilation
• Simulating normalizing constants: From importance sampling to bridge sampling to path sampling
• Nested sampling for general Bayesian computation
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