Quantum Mechanics for Closure of Dynamical Systems

DOI10.1137/22m1514246arXiv2208.03390OpenAlexW4391539063MaRDI QIDQ6150480

Dimitrios Giannakis, Joanna Slawinska, Unnamed Author

Publication date: 6 March 2024

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

Full work available at URL: https://arxiv.org/abs/2208.03390

zbMATH Keywords

quantum mechanicskernel methodstransfer operatorsparameterizationKoopman operatorsdynamical closure

Mathematics Subject Classification ID

Cites Work

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• Analysis of the 3DVAR filter for the partially observed Lorenz '63 model
• Data-driven model reduction and transfer operator approximation
• Modeling of missing dynamical systems: deriving parametric models using a nonparametric framework
• A data-driven approximation of the koopman operator: extending dynamic mode decomposition
• Variable bandwidth diffusion kernels
• Kernel methods in machine learning
• CRCP: a cloud resolving convection parameterization for modeling the tropical convecting atmosphere
• Embedology
• Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification
• A computational strategy for multiscale systems with applications to Lorenz 96 model
• Reproducing kernel Hilbert space compactification of unitary evolution groups
• Model reduction and neural networks for parametric PDEs
• Supervised learning from noisy observations: combining machine-learning techniques with data assimilation
• Data-driven approximation of the Koopman generator: model reduction, system identification, and control
• Kernel flows: from learning kernels from data into the abyss
• Delay-coordinate maps, coherence, and approximate spectra of evolution operators
• Bi-stochastic kernels via asymmetric affinity functions
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• Delay-coordinate maps and the spectra of Koopman operators
• Data-driven spectral decomposition and forecasting of ergodic dynamical systems
• Deterministic homogenization for fast-slow systems with chaotic noise
• Consistency of spectral clustering
• Spectral properties of dynamical systems, model reduction and decompositions
• Statistical Structure of Quantum Theory
• A note on diffusion limits of chaotic skew-product flows
• Young Measure Approach to Computing Slowly Advancing Fast Oscillations
• Spectral analysis of nonlinear flows
• Hamiltonian Systems and Transformation in Hilbert Space
• Models for stochastic climate prediction
• On the isolated spectrum of the Perron-Frobenius operator
• Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks
• Data-Driven Discovery of Closure Models
• Ergodic averaging with and without invariant measures
• Deterministic Nonperiodic Flow
• The Quantum Theory of Measurement
• Kernel Analog Forecasting: Multiscale Test Problems
• Learning stochastic closures using ensemble Kalman inversion
• Autodifferentiable Ensemble Kalman Filters
• Cryptocurrencies: Protocols for Consensus
• Efficient stochastic superparameterization for geophysical turbulence
• Data-driven discovery of coordinates and governing equations
• Operator Theoretic Aspects of Ergodic Theory
• Data Assimilation
• Graph Laplacian Tomography From Unknown Random Projections
• Filtering Complex Turbulent Systems
• Universality, Characteristic Kernels and RKHS Embedding of Measures
• Multiscale Methods
• Geometry of Quantum States
• Large Eddy Simulation for Incompressible Flows
• Theory of operator algebras I.
• A framework for machine learning of model error in dynamical systems
• Learning to Forecast Dynamical Systems from Streaming Data

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