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On mean subtraction and Dynamic Mode Decomposition
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Classroom Contents
Data-Driven Modelling - Machine Learning and Dynamical Systems
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- 1 Interpretable and Generalizable Machine Learning for Modeling and Control
- 2 Learning Missing Dynamics from Data
- 3 Collective variables in complex systems
- 4 Data-driven learning of control signals, parameters, and governing equations
- 5 Big Data and Machine Learning for Analysis of Numerical PDEs
- 6 Data-driven PDE modelling: Trick or treat!
- 7 Hidden Markov Models and Dynamical Systems
- 8 Optimising linear response of kernel dynamics and transfer operator extraction of the ENSO cycle
- 9 Data-Driven Prediction of Multistable Systems from Sparse Measurements
- 10 Output-Weighted Active Sampling for Uncertainty Quantification and Prediction of Rare Events
- 11 Data driven model reduction and the Koopman-Mori-Zwanzig formalism
- 12 Challenges for Building Surrogate Model for Nuclear Reaction Systems
- 13 Interpreted machine learning in fluid dynamics: Explaining relaminarisation events in wall-bounded
- 14 Recurrent Neural Networks for Spatiotemporal Prediction of Chaotic Dynamics
- 15 Data-driven approximation of the Koopman generator and Schrödinger operator
- 16 Data Driven Port Hamiltonian systems modelling and control
- 17 Learning Dynamical Systems with Side Information
- 18 Supervised learning from noisy observations
- 19 Probabilistic aggregation of large under-sampled Markov chains
- 20 Machine-learning of model error in ODEs
- 21 Modeling synchronization in forced turbulent oscillator flows
- 22 SINDy-PI: A Robust Algorithym for Parallel Implicit Sparse Identification of Nonlinear Dynamics
- 23 On mean subtraction and Dynamic Mode Decomposition
- 24 Machine learning enablers for system optimization and design
- 25 Transforming Signals to Images Using Attractor Reconstruction for Deep Learning
- 26 Gedmd: Data-Driven Analysis of Stochastic Dynamical Systems
- 27 Linear response for the dynamic Laplacian and finite-time coherent sets
- 28 Learning sparse dynamics of interacting systems
- 29 Variational methods and deep learning for high-dimensional dynamical systems
- 30 Resampling with neural networks for data-driven stochastic parameterization
- 31 Nonparametric Nonlinear Model Reduction for slow-fast SDEs near manifolds
- 32 Learning Dynamical Systems using Local Stability Priors
