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Found 6 papers, 0 papers with code
Short- and long-term prediction of a chaotic flow: A physics-constrained reservoir computing approach
We propose a physics-constrained machine learning method-based on reservoir computing- to time-accurately predict extreme events and long-term velocity statistics in a model of turbulent shear flow.
Auto-Encoded Reservoir Computing for Turbulence Learning
We present an Auto-Encoded Reservoir-Computing (AE-RC) approach to learn the dynamics of a 2D turbulent flow.
Physics-Informed Echo State Networks
This approach is demonstrated on a chaotic Lorenz system and a truncation of the Charney-DeVore system.
Learning Hidden States in a Chaotic System: A Physics-Informed Echo State Network Approach
We extend the Physics-Informed Echo State Network (PI-ESN) framework to reconstruct the evolution of an unmeasured state (hidden state) in a chaotic system.
A physics-aware machine to predict extreme events in turbulence
We propose a physics-aware machine learning method to time-accurately predict extreme events in a turbulent flow.
Physics-Informed Echo State Networks for Chaotic Systems Forecasting
We propose a physics-informed Echo State Network (ESN) to predict the evolution of chaotic systems.
