Information geometry of physics-informed statistical manifolds and its use in data assimilation

DOI10.1016/j.jcp.2022.111438OpenAlexW3135119210WikidataQ114163249 ScholiaQ114163249MaRDI QIDQ2162022

F. Boso, Daniel M. Tartakovsky

Publication date: 5 August 2022

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2103.01160

zbMATH Keywords

parameter identificationBayesian inferencemachine learningmethod of distributions

Mathematics Subject Classification ID

Mathematical programming (90Cxx) Artificial intelligence (68Txx) Probabilistic methods, stochastic differential equations (65Cxx)

Related Items (1)

Feature-informed data assimilation

Uses Software

• EnKF
• L-BFGS
• FiPy
• JiTCDDE
• JiTCODE
• JiTCSDE
• PRMLT

Cites Work

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• Fundamental limitations of polynomial chaos for uncertainty quantification in systems with intermittent instabilities
• Wasserstein geometry of Gaussian measures
• Information geometry and its applications
• On the limited memory BFGS method for large scale optimization
• Information geometry connecting Wasserstein distance and Kullback-Leibler divergence via the entropy-relaxed transportation problem
• Natural gradient via optimal transport
• Data-driven distributionally robust optimization using the Wasserstein metric: performance guarantees and tractable reformulations
• Online natural gradient as a Kalman filter
• The Fokker-Planck equation. Methods of solutions and applications.
• Data-driven discovery of coarse-grained equations
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• Adaptive methods for stochastic differential equations via natural embeddings and rejection sampling with memory
• A Bayesian tutorial for data assimilation
• Nonlocal PDF methods for Langevin equations with colored noise
• Stochastic processes and filtering theory
• Consequences of weak Allee effect on prey in the May-Holling-Tanner predator-prey model
• Algorithms for PDE-constrained optimization
• Efficiently and easily integrating differential equations with JiTCODE, JiTCDDE, and JiTCSDE
• Dynamics of Data-driven Ambiguity Sets for Hyperbolic Conservation Laws with Uncertain Inputs
• Learning on dynamic statistical manifolds
• Data-Informed Method of Distributions for Hyperbolic Conservation Laws
• Information-Geometric Optimization Algorithms: A Unifying Picture via Invariance Principles
• The ensemble Kalman filter for combined state and parameter estimation
• Consistent Estimates Based on Partially Consistent Observations

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