15 citations · 33 across the 8 of their papers we have counts for
6 papers · 1 filter
A data driven approach to classify descriptors based on their efficiency in translating noisy trajectories into physically-relevant information
Simone Martino, Domiziano Doria, Chiara Lionello +2
Reconstructing the physical complexity of many-body dynamical systems can be challenging. Starting from the trajectories of their constitutive units (raw data), typical approaches…
Data-driven assessment of optimal spatiotemporal resolutions for information extraction in noisy time series data
Domiziano Doria, Simone Martino, Matteo Becchi +1
In general, comprehension of any type of complex system depends on the resolution used to examine the phenomena occurring within it. However, identifying a priori, for example, the…
Relevant, hidden, and frustrated information in high-dimensional analyses of complex dynamical systems with internal noise
Chiara Lionello, Matteo Becchi, Simone Martino +1
Extracting from trajectory data meaningful information to understand complex molecular systems might be non-trivial. High-dimensional analyses are typically assumed to be desirable…
Classification and Spatiotemporal Correlation of Dominant Fluctuations in Complex Dynamical Systems
Cristina Caruso, Martina Crippa, Annalisa Cardellini +4
The behavior of many complex systems, from nanostructured materials to animal colonies, is governed by local transitions that, while involving a restricted number of interacting un…
Unsupervised Tracking of Local and Collective Defects Dynamics in Metals Under Deformation
Matteo Cioni, Mattia Perrone, Massimo Delle Piane +1
Metals owe their unique mechanical properties to how defects emerge and propagate within their crystal structure under stress. However, the mechanisms leading from the early emergi…
"Layer-by-layer" Unsupervised Clustering of Statistically Relevant Fluctuations in Noisy Time-series Data of Complex Dynamical Systems
Matteo Becchi, Federico Fantolino, Giovanni M. Pavan
Complex systems are typically characterized by intricate internal dynamics that are often hard to elucidate. Ideally, this requires methods that allow to detect and classify in uns…