Tootfinder

Minimal Deterministic Echo State Networks Outperform Random Reservoirs in Learning Chaotic Dynamics
Francesco Martinuzzi
https://arxiv.org/abs/2507.06050 h…

Minimal Deterministic Echo State Networks Outperform Random Reservoirs in Learning Chaotic Dynamics
Machine learning (ML) is widely used to model chaotic systems. Among ML approaches, echo state networks (ESNs) have received considerable attention due to their simple construction and fast training. However, ESN performance is highly sensitive to hyperparameter choices and to its random initialization. In this work, we demonstrate that ESNs constructed using deterministic rules and simple topologies (MESNs) outperform standard ESNs in the task of chaotic attractor reconstruction. We use a data…

Topological characterisation of a chaotic attractor with an additional branch generated from economic data
Alexandre Meneceur, Vincent Lignon, Martin Rosalie
https://arxiv.org/abs/2507.04810

Topological characterisation of a chaotic attractor with an additional branch generated from economic data
There are insights of chaotic properties in economic systems and data. To prove the existence of chaotic dynamics, the establishment of a deterministic model is mandatory. A global modelling tool (GPoM) is used to search for mathematical models of equations from economic data: unemployment, inflation and nominal exchange rate over 30 years. A system of three differential equations is chosen as a model, whose solution is a chaotic attractor in $\mathbb{R}^3$. The model extracted from the data is…