5 papers
Understanding the superiority of multi-model ensemble forecasts through reservoir computing
Daniel Estevez Moya, Francesco Martinuzzi, Edmilson Roque dos Santos +3
Weather forecasting and climate projection frequently use multi-model ensembles (MMEs) to improve short-term forecasts by averaging across models. However, this practice is often n…
Storage and selection of multiple chaotic attractors in minimal reservoir computers
Francesco Martinuzzi, Holger Kantz
Modern predictive modeling increasingly calls for a single learned dynamical substrate to operate across multiple regimes. From a dynamical-systems viewpoint, this capability decom…
Minimal Deterministic Echo State Networks Outperform Random Reservoirs in Learning Chaotic Dynamics
Francesco Martinuzzi
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…
A Sensitivity Analysis of Cellular Automata and Heterogeneous Topology Networks: Partially-Local Cellular Automata and Homogeneous Homogeneous Random Boolean Networks
Tom Eivind Glover, Ruben Jahren, Francesco Martinuzzi +2
Elementary Cellular Automata (ECA) are a well-studied computational universe that is, despite its simple configurations, capable of impressive computational variety. Harvesting thi…
Composing Modeling and Simulation with Machine Learning in Julia
Chris Rackauckas, Ranjan Anantharaman, Alan Edelman +10
In this paper we introduce JuliaSim, a high-performance programming environment designed to blend traditional modeling and simulation with machine learning. JuliaSim can build acce…