Learning flow functions of spiking systems

no code implementations • 19 Dec 2023 • Miguel Aguiar, Amritam Das, Karl H. Johansson

We propose a framework for surrogate modelling of spiking systems.

Universal approximation of flows of control systems by recurrent neural networks

no code implementations • 1 Apr 2023 • Miguel Aguiar, Amritam Das, Karl H. Johansson

In this paper, we prove that an architecture based on discrete-time recurrent neural networks universally approximates flows of continuous-time dynamical systems with inputs.

Learning Flow Functions from Data with Applications to Nonlinear Oscillators

no code implementations • 29 Mar 2023 • Miguel Aguiar, Amritam Das, Karl H. Johansson

We show that the proposed architecture is able to approximate the flow function by exploiting the system's causality and time-invariance.