Implementation of the paper Learning stochastic dynamical systems with neural networks mimicking the Euler-Maruyama scheme.
Associated paper:
License: CECILL-C license Copyright IMT Atlantique/OceaniX, contributor(s) : Noura Dridi, Lucas Drumetz, Ronan Fablet 18/05/2021
Contact person: nourradridi@gmail.com
This software is a computer program aims to learn the parameters of a Stochastic Differential Equation (SDE) https://en.wikipedia.org/wiki/Stochastic_differential_equation used to model stochastic dynamical sytem. The parameters of the SDE are represented by a neural network with a built-in SDE integration scheme using on Euler Maruyama method https://en.wikipedia.org/wiki/Euler%E2%80%93Maruyama_method . The software is availble for one dimension example Geometric Brownian Motion https://en.wikipedia.org/wiki/Geometric_Brownian_motion , as well as three dimensional one called Stochastic Lorenz. The latter is a stochastic version of the Lorenz-63 system well known to represent ocean-atmosphere interactions derived from the Navier-Stokes equations https://hal.inria.fr/hal-01629898.
Citation : @article{Dridi2021, title={Learning stochastic dynamical systems with neural networks mimicking the Euler-Maruyama scheme}, author={Noura, Dridi and Lucas, Drumetz and Ronan Fablet}, journal={European Signal Processing Conference, EUSIPCO}, year={2021} }