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In many complex molecular systems, the macroscopic ensemble's properties are controlled by microscopic dynamic events (or fluctuations) that are often difficult to detect via pattern-recognition approaches. Discovering the relationships…

Chemical Physics · Physics 2023-09-01 Martina Crippa , Annalisa Cardellini , Matteo Cioni , Gábor Csányi , Giovanni M. Pavan

Many molecular systems and physical phenomena are controlled by local fluctuations and microscopic dynamical rearrangements of the constitutive interacting units that are often difficult to detect. This is the case, for example, of phase…

Despite great efforts over the past 50 years, the simulation of water still presents significant challenges and open questions. At room temperature and pressure, the collective molecular interactions and dynamics of water molecules may form…

Chemical Physics · Physics 2022-06-22 Riccardo Capelli , Francesco Muniz-Miranda , Giovanni M. Pavan

Our goal is to recover time-delayed latent causal variables and identify their relations from measured temporal data. Estimating causally-related latent variables from observations is particularly challenging as the latent variables are not…

Machine Learning · Statistics 2022-02-10 Weiran Yao , Yuewen Sun , Alex Ho , Changyin Sun , Kun Zhang

Many complex molecular systems owe their properties to local dynamic rearrangements or fluctuations that, despite the rise of machine learning (ML) and sophisticated structural descriptors, remain often difficult to detect. Here we show an…

Chemical Physics · Physics 2023-08-16 Martina Crippa , Annalisa Cardellini , Cristina Caruso , Giovanni M. Pavan

We propose a novel neural network embedding approach to model power transmission grids, in which high voltage lines are disconnected and reconnected with one-another from time to time, either accidentally or willfully. We call our…

Signal Processing · Electrical Eng. & Systems 2019-08-23 Benjamin Donnot , Balthazar Donon , Isabelle Guyon , Zhengying Liu , Antoine Marot , Patrick Panciatici , Marc Schoenauer

Temporal networks model how the interaction between elements in a complex system evolve over time. Just like complex systems display collective dynamics, here we interpret temporal networks as trajectories performing a collective motion in…

Social and Information Networks · Computer Science 2022-10-18 Lucas Lacasa , Jorge P. Rodriguez , Victor M. Eguiluz

Stories about everyday situations are an essential part of human communication, motivating the need to develop AI agents that can reliably understand these stories. Despite the long list of supervised methods for story completion and…

Computation and Language · Computer Science 2023-11-21 Yifan Jiang , Filip Ilievski , Kaixin Ma

We introduce a new and robust approach for characterizing spatially and temporally heterogeneous behavior within a system based on the evolution of dynamic fuctuations once averaged over different space lengths and time scales. We apply it…

Disordered Systems and Neural Networks · Physics 2018-07-04 J. Ariel Rodriguez Fris , Eric R. Weeks , Francesco Sciortino , Gustavo A. Appignanesi

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 emerging (local) defects to the amplification of dislocations…

Materials Science · Physics 2025-02-14 Matteo Cioni , Mattia Perrone , Massimo Delle Piane , Giovanni Maria Pavan

The evolution of many dynamical systems that describe relationships or interactions between objects can be effectively modeled by temporal networks, which are typically represented as a sequence of static network snapshots. In this paper,…

Social and Information Networks · Computer Science 2025-07-11 Filip Blašković , Tim O. F. Conrad , Stefan Klus , Nataša Djurdjevac Conrad

Human cognition has compositionality. We understand a scene by decomposing the scene into different concepts (e.g., shape and position of an object) and learning the respective laws of these concepts, which may be either natural (e.g., laws…

Computer Vision and Pattern Recognition · Computer Science 2023-02-28 Fan Shi , Bin Li , Xiangyang Xue

Most complex systems are intrinsically dynamic in nature. The evolution of a dynamic complex system is typically represented as a sequence of snapshots, where each snapshot describes the configuration of the system at a particular instant…

Physics and Society · Physics 2016-12-30 Richard K. Darst , Clara Granell , Alex Arenas , Sergio Gómez , Jari Saramäki , Santo Fortunato

This paper introduces the Theory of the Unique Latent Pattern (ULP), a formal epistemic framework that redefines the origin of apparent complexity in dynamic systems. Rather than attributing unpredictability to intrinsic randomness or…

Artificial Intelligence · Computer Science 2025-05-27 Mohamed Aly Bouke

The recently introduced Theory of the Adjacent Possible (TAP) is a model of combinatorial innovation aiming to explain the "hockey-stick" upward trend of human technological evolution, where an explosion in the number of produced items with…

Populations and Evolution · Quantitative Biology 2023-09-07 M. Andrecut

A complex system with cluttered observations may be a coupled mixture of multiple simple sub-systems corresponding to latent entities. Such sub-systems may hold distinct dynamics in the continuous-time domain; therein, complicated…

Machine Learning · Computer Science 2025-02-17 Zihan Zhou , Tianshu Yu

Machine Learning (ML) is becoming increasingly popular in fluid dynamics. Powerful ML algorithms such as neural networks or ensemble methods are notoriously difficult to interpret. Here, we introduce the novel Shapley Additive Explanations…

Fluid Dynamics · Physics 2022-05-20 Martin Lellep , Jonathan Prexl , Bruno Eckhardt , Moritz Linkmann

Disordered materials under an imposed forcing can display creep and aging effects, accompanied by intermittent, spatially heterogeneous dynamics. We propose a unifying microscopic description of these phenomena, based on the notion that as…

Disordered Systems and Neural Networks · Physics 2024-09-27 Daniel J. Korchinski , Dor Shohat , Yoav Lahini , Matthieu Wyart

Extracting from trajectory data meaningful information to understand complex molecular systems might be non-trivial. High-dimensional analyses are typically assumed to be desirable, if not required, to prevent losing important information.…

Chemical Physics · Physics 2025-12-01 Chiara Lionello , Matteo Becchi , Simone Martino , Giovanni M. Pavan

Understanding the flow behaviors of supercooled liquids presents a major challenge in liquid-state physics due to the strong nonlinearity and rich phenomena. To unravel this complexity, we introduce the concept of local configurational…

Soft Condensed Matter · Physics 2025-11-25 Dong-Xu Yu , Ke-Qi Zeng , Zhe Wang
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