23 citations · 55 across the 8 of their papers we have counts for
9 papers
Learning the information contained in the microstructure of data distributions
Lucrezia L. Baldo, Chiara Lionello, Edward D. Donkor +2
Understanding how much and where information is nested in sets of data obtained using different parameters and metrics is a typical problem in the analysis of high-dimensional data…
Physically-Relevant Information Learning in High-Dimensional Time-Derivatives Spaces
Domiziano Doria, Matteo Becchi, Giovanni M. Pavan
Understanding the physics of many-body complex dynamical systems may be a non-trivial task. High-dimensional analysis approaches are often deemed necessary to prevent losing import…
dynsight: an Open Python Platform for Simulation and Experimental Trajectory Data Analysis
Simone Martino, Matteo Becchi, Andrew Tarzia +2
The study of complex many-body systems via analysis of the trajectories of the units that dynamically move and interact within them is a non-trivial task. The workflow for extracti…
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…
"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…
Molecular layers in thin supported films exhibit the same scaling as the bulk between slow relaxation and vibrational dynamics
Matteo Becchi, Andrea Giuntoli, Dino Leporini
We perform molecular dynamics simulations of a supported molecular thin film. By varying thickness and temperature, we observe anisotropic mobility as well as strong gradients of b…