Learning Chaotic Systems and Long-Term Predictions with Neural Jump ODEs

• URL: http://arxiv.org/abs/2407.18808v1
• Date: Fri, 26 Jul 2024 15:18:29 GMT
• Title: Learning Chaotic Systems and Long-Term Predictions with Neural Jump ODEs
• Authors: Florian Krach, Josef Teichmann,
• Abstract summary: Pathdependent Neural Jump ODE (PDNJ-ODE) is a model for online prediction of generic processes with irregular (in time) and potentially incomplete (with respect to coordinates) observations. In this work we enhance the model with two novel ideas, which independently of each other improve the performance of our modelling setup. The same enhancements can be used to provably enable the PDNJ-ODE to learn long-term predictions for general datasets, where the standard model fails.
• Score: 4.204990010424083
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Path-dependent Neural Jump ODE (PD-NJ-ODE) is a model for online prediction of generic (possibly non-Markovian) stochastic processes with irregular (in time) and potentially incomplete (with respect to coordinates) observations. It is a model for which convergence to the $L^2$-optimal predictor, which is given by the conditional expectation, is established theoretically. Thereby, the training of the model is solely based on a dataset of realizations of the underlying stochastic process, without the need of knowledge of the law of the process. In the case where the underlying process is deterministic, the conditional expectation coincides with the process itself. Therefore, this framework can equivalently be used to learn the dynamics of ODE or PDE systems solely from realizations of the dynamical system with different initial conditions. We showcase the potential of our method by applying it to the chaotic system of a double pendulum. When training the standard PD-NJ-ODE method, we see that the prediction starts to diverge from the true path after about half of the evaluation time. In this work we enhance the model with two novel ideas, which independently of each other improve the performance of our modelling setup. The resulting dynamics match the true dynamics of the chaotic system very closely. The same enhancements can be used to provably enable the PD-NJ-ODE to learn long-term predictions for general stochastic datasets, where the standard model fails. This is verified in several experiments.

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