5 papers
A rigorous data-driven approach to the nucleation of defects in metals exploiting the link between kinetic properties and (dis)order parameters
Mattia Perrone, David D. Girardier, Giovanni M. Pavan +1
Nucleation processes, through which a new structure progressively forms within a pre-existing homogeneous phase, are fundamental in materials science, but are also typically non-tr…
Maximum Information Extraction Via Clustering and Minimization of Shannon Entropy
Matteo Becchi, Giovanni Maria Pavan
In the analysis of any type of system, granting maximum information extraction from its data is non-trivial. Confidence in successful information extraction typically builds on pri…
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…