arXiv subjects

Domiziano Doria

Publications and source records attributed to Domiziano Doria.

3 recordsLinked to original sources

arXiv2026

Physically-Relevant Information Learning in High-Dimensional Time-Derivatives Spaces

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 important information. Typically, these use order parameters or descriptors capturing information related to, e.g., relative positions, symmetries, etc., of the units in the studied system. However, in many cases, gaining information related to the relative positions of the constitutive units (or their velocities) alone may be insufficient, and to reach a more complete physical knowledge, one should ideally learn and correlate with each other both structure and dynamics. Here we demonstrate how to achieve such a goal efficiently by building and navigating high-dimensional Time-Derivatives (TiDe) spaces. A TiDe space can be generated for virtually any type of system/phenomenon from the time-series data collected along its observation over time. Each TiDe's dimension corresponds to a growing-order time-derivative of the extracted data, thus containing information related to different physical phenomena/events, which can be easily extracted via unsupervised approaches. We demonstrate how, by definition, TiDes can be directly analyzed without a need for prior dimensionality reduction, providing results that are intrinsically intuitive to interpret. We show the potential of the method by analyzing two prototypical example datasets extracted from molecular dynamics simulations or experimental tracking of different types of complex dynamical systems. Our results demonstrate how efficiently one can navigate and learn in information-rich TiDe spaces, which provide a robust general framework for data analysis and for studying complex dynamical systems from the data collected along their observation over time.

physics.chem-ph↗

arXiv2025

Data-driven assessment of optimal spatiotemporal resolutions for information extraction in noisy time series data

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 best time frequencies/scales to study a certain system over-time, or the spatial distances at which correlations, symmetries, and fluctuations are, most often non-trivial. Here we describe an unsupervised approach that, starting solely from the data of a system, allows learning the characteristic length scales of the dominant key events/processes and the optimal spatiotemporal resolutions to characterize them. We tested this approach on time series data obtained from simulation or experimental trajectories of various example many-body complex systems ranging from the atomic to the macroscopic scale and having diverse internal dynamic complexities. Our method automatically analyzes the system data by analyzing correlations at all relevant inter-particle distances and at all possible inter-frame intervals in which their time series can be subdivided, namely, at all space and time resolutions. The optimal spatiotemporal resolution for studying a certain system thus maximizes information extraction and classification from the system's data, which we prove to be related to the characteristic spatiotemporal length scales of the local/collective physical events dominating it. This approach is broadly applicable and can be used to optimize the study of different types of data (static distributions, time series, or signals). The concept of 'optimal resolution' has a general character and provides a robust basis for characterizing any type of system based on its data, as well as to guide data analysis in general.

physics.data-an↗

arXiv2024

A data driven approach to classify descriptors based on their efficiency in translating noisy trajectories into physically-relevant information

Reconstructing the physical complexity of many-body dynamical systems can be challenging. Starting from the trajectories of their constitutive units (raw data), typical approaches require selecting appropriate descriptors to convert them into time-series, which are then analyzed to extract interpretable information. However, identifying the most effective descriptor is often non-trivial. Here, we report a data-driven approach to compare the efficiency of various descriptors in extracting information from noisy trajectories and translating it into physically relevant insights. As a prototypical system with non-trivial internal complexity, we analyze molecular dynamics trajectories of an atomistic system where ice and water coexist in equilibrium near the solid/liquid transition temperature. We compare general and specific descriptors often used in aqueous systems: number of neighbors, molecular velocities, Smooth Overlap of Atomic Positions (SOAP), Local Environments and Neighbors Shuffling (LENS), Orientational Tetrahedral Order, and distance from the fifth neighbor ($d_5$). Using Onion Clustering -- an efficient unsupervised method for single-point time-series analysis -- we assess the maximum extractable information for each descriptor and rank them via a high-dimensional metric. Our results show that advanced descriptors like SOAP and LENS outperform classical ones due to higher signal-to-noise ratios. Nonetheless, even simple descriptors can rival or exceed advanced ones after local signal denoising. For example, $d_5$, initially among the weakest, becomes the most effective at resolving the system's non-local dynamical complexity after denoising. This work highlights the critical role of noise in information extraction from molecular trajectories and offers a data-driven approach to identify optimal descriptors for systems with characteristic internal complexity.

cond-mat.mtrl-sci↗