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We use a semi-supervised, neural-network based machine learning technique, the confusion method, to investigate structural transitions in magnetic polymers, which we model as chains of magnetic colloidal nanoparticles characterized by…

Soft Condensed Matter · Physics 2025-06-27 Dilina Perera , Samuel McAllister , Joan Josep Cerdà , Thomas Vogel

In this paper, we introduce a novel mechanism that uses machine learning techniques to detect water leaks in pipes. The proposed simple and low-cost mechanism is designed that can be easily installed on building pipes with various sizes.…

Sound · Computer Science 2025-01-22 Hossein Pourmehrani , Reshad Hosseini , Hadi Moradi

In this paper a data analytical approach featuring support vector machines (SVM) is employed to train a predictive model over an experimentaldataset, which consists of the most relevant studies for two-phase flow pattern prediction. The…

Machine Learning · Statistics 2018-06-14 Pablo Guillen-Rondon , Melvin D. Robinson , Carlos Torres , Eduardo Pereya

Being able to predict the failure of materials based on structural information is a fundamental issue with enormous practical and industrial relevance for the monitoring of devices and components. Thanks to recent advances in deep learning,…

Peridynamics provides a versatile tool for fracture modelling in materials where fracture pathways cannot be predicted beforehand, but must be envisaged as an emergent features of the deformation process. One class of materials where this…

Materials Science · Physics 2025-12-18 Shucheta Shegufta , Michael Zaiser

Machine-learning methods are nowadays of common use in the field of material science. For example, they can aid in optimizing the physicochemical properties of new materials, or help in the characterization of highly complex chemical…

Disordered Systems and Neural Networks · Physics 2022-11-29 Maciej J. Karcz , Luca Messina , Eiji Kawasaki , Serenah Rajaonson , Didier Bathellier , Emeric Bourasseau

Amorphous solids exhibit quasi-universal low-temperature anomalies whose origin has been ascribed to localized tunneling defects. Using an advanced Monte Carlo procedure, we create {\it in silico} glasses spanning from hyperquenched to…

Disordered Systems and Neural Networks · Physics 2020-06-17 Dmytro Khomenko , Camille Scalliet , Ludovic Berthier , David R. Reichman , Francesco Zamponi

We introduce a system of shallow water-type equations to model laboratory experiments of particle-laden flows. We explore homogeneous liquid-solid suspensions of fine, non-cohesive, monodisperse glass beads which propagate as an equivalent…

Fluid Dynamics · Physics 2025-02-11 Andrea Bondesan , Laurence Girolami , François James , Loïc Rousseau

It has been a long-standing materials science challenge to establish structure-property relations in amorphous solids. Here we introduce a rotation-variant local structure representation that enables different predictions for different…

Materials Science · Physics 2022-03-15 Zhao Fan , Evan Ma

The anomalous behavior of liquid water is widely associated with a liquid-liquid phase transition between high- and low-density states in the supercooled regime. At the microscopic level, tetrahedral hydrogen-bond networks govern these…

Soft Condensed Matter · Physics 2026-05-04 Kohei Yoshikawa , Kokoro Shikata , Kang Kim , Nobuyuki Matubayasi

Frictional instabilities in fluid saturated granular materials underlie natural hazards, including submarine landslides and earthquake initiation. Experiments show distinct failure behaviors under subaerial and subaqueous conditions due to…

Soft Condensed Matter · Physics 2025-12-25 Bimal Chhushyabaga , Behrooz Ferdowsi

Controlling the mechanical response of soft glassy materials, such as emulsions, foams, and colloidal suspensions, is key for many industrial processes. While their steady-state flow behavior is reasonably well understood, their response to…

Soft Condensed Matter · Physics 2025-08-08 Frédéric Blanc , Guillaume Ovarlez , Adam Trigui , Kirsten Martens , Romain Mari

Detecting structures at the particle scale within plastically deformed crystalline materials allows a better understanding of the occurring phenomena. While previous approaches mostly relied on applying hand-chosen criteria on different…

Materials Science · Physics 2024-05-15 Armand Barbot , Riccardo Gatti

We derive a mode-coupling theory for the slow dynamics of fluids confined in disordered porous media represented by spherical particles randomly placed in space. Its equations display the usual nonlinear structure met in this theoretical…

Soft Condensed Matter · Physics 2007-05-23 V. Krakoviack

The sudden arrest of flow by formation of a stable arch over an outlet is a unique and characteristic feature of granular materials. Previous work suggests that grains near the outlet randomly sample configurational flow microstates until a…

Soft Condensed Matter · Physics 2024-07-09 Jesse M. Hanlan , Sam Dillavou , Andrea J. Liu , Douglas J. Durian

Complex crystal structures are composed of multiple local environments, and how this type of order emerges spontaneously during crystal growth has yet to be fully understood. We study crystal growth across various structures and along…

Soft Condensed Matter · Physics 2024-06-03 Maya M. Martirossyan , Matthew Spellings , Hillary Pan , Julia Dshemuchadse

Complex fluids such as emulsions, colloidal gels, polymer or surfactant solutions are all characterized by the existence of a "microstructure" which may couple to an external flow on timescales that are easily probed in experiments. Such a…

Soft Condensed Matter · Physics 2015-02-11 Christophe Perge , Marc-Antoine Fardin , Sebastien Manneville

We investigate the local structural fluctuations of a model equilibrium fluid with an aim of better understanding the structural basis of locally heterogeneous dynamics identified in recent simulations and experimental studies of…

Soft Condensed Matter · Physics 2018-11-14 Tamoghna Das , Jack F. Douglas

The growing interest in creating a parametric representation of liquid sloshing inside a container stems from its practical applications in modern engineering systems. The resonant excitation, on the other hand, can cause unstable and…

Machine Learning · Computer Science 2022-01-28 Xihaier Luo , Ahsan Kareem , Liting Yu , Shinjae Yoo

Understanding the three-dimensional motion of bubbles is essential for interpreting transport and mixing in multiphase flows, especially when bubbles deform under shear or move rapidly through the flow field. In many laboratory setups, only…

Fluid Dynamics · Physics 2026-01-30 Chaitanya S Nayak , Faizaan Mohammed , Vivek Kumar , Shivam Prajapati , Cyrus Aidun
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