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Found 4 papers, 2 papers with code
Stabilizing Machine Learning Prediction of Dynamics: Noise and Noise-inspired Regularization
In this article, we systematically examine the technique of adding noise to the ML model input during training to promote stability and improve prediction accuracy.
Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Components
We show that by using partial measurements of the state of the dynamical system, we can train a machine learning model to improve predictions made by an imperfect knowledge-based model.
Backpropagation Algorithms and Reservoir Computing in Recurrent Neural Networks for the Forecasting of Complex Spatiotemporal Dynamics
We examine the efficiency of Recurrent Neural Networks in forecasting the spatiotemporal dynamics of high dimensional and reduced order complex systems using Reservoir Computing (RC) and Backpropagation through time (BPTT) for gated network architectures.
Using Machine Learning to Replicate Chaotic Attractors and Calculate Lyapunov Exponents from Data
For the case of the KS equation, we note that as the system's spatial size is increased, the number of Lyapunov exponents increases, thus yielding a challenging test of our method, which we find the method successfully passes.
Chaotic Dynamics
