most citedUniversal approximation of flows of control systems by recurrent neural networks

1 citations · 1 across the 3 of their papers we have counts for

collaborators

3 papers

eess.SY2023

Learning Flow Functions from Data with Applications to Nonlinear Oscillators

Miguel Aguiar, Amritam Das, Karl H. Johansson

We describe a recurrent neural network (RNN) based architecture to learn the flow function of a causal, time-invariant and continuous-time control system from trajectory data. By r…

eess.SY2023★ 1 cited

Universal approximation of flows of control systems by recurrent neural networks

Miguel Aguiar, Amritam Das, Karl H. Johansson

We consider the problem of approximating flow functions of continuous-time dynamical systems with inputs. It is well-known that continuous-time recurrent neural networks are univer…

math.OC2021

Physics-informed Learning for Identification and State Reconstruction of Traffic Density

Matthieu Barreau, Miguel Aguiar, John Liu +1

We consider the problem of traffic density reconstruction using measurements from probe vehicles (PVs) with a low penetration rate. In other words, the number of sensors is small c…