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Recent studies have revealed that neural networks learn interpretable algorithms for many simple problems. However, little is known about how these algorithms emerge during training. In this article, I study the training dynamics of a small…

Machine Learning · Computer Science 2024-10-29 Tiberiu Musat

Grain growth simulation is crucial for predicting metallic material microstructure evolution during annealing and resulting final mechanical properties, but traditional partial differential equation-based methods are computationally…

Materials Science · Physics 2025-05-09 Pungponhavoan Tep , Marc Bernacki

We investigate the formation of stress hotspots in polycrystalline materials under uniaxial tensile deformation by integrating full field crystal plasticity based deformation models and machine learning techniques to gain data driven…

Materials Science · Physics 2018-06-15 Ankita Mangal , Elizabeth A. Holm

Asymmetric obstacles can be exploited to direct the motion and induce sorting of run-and-tumbling particles. In this work, we show that flocking particles which follow the Vicsek model aligning rules experience a collective trapping in the…

Soft Condensed Matter · Physics 2019-12-02 Raul Martinez , Francisco Alarcon , Juan Luis Aragones , Chantal Valeriani

We study the rheology of cornstarch suspensions, a non-Brownian particle system that exhibits discontinuous shear thickening. Using magnetic resonance imaging (MRI), the local properties of the flow are obtained by the determination of…

Soft Condensed Matter · Physics 2012-06-11 Abdoulaye Fall , François Bertrand , Guillaume Ovarlez , Daniel Bonn

Very soft grain assemblies have unique shape-changing capabilities that allow them to be compressed far beyond the rigid jammed state by filling void spaces more effectively. However, accurately following the formation of these systems by…

Metal-organic frameworks (MOFs) are excellent candidates for water harvesting due to their tunable pore environments, which can be precisely engineered to capture and release water in arid conditions. Integrating artificial intelligence…

Materials Science · Physics 2026-05-29 Reid A. Coyle , Shyam Chand Pal , Peter Walther , Saeun Park , Bin Feng , Zhiling Zheng

Compressive learning is an emerging approach to drastically reduce the memory footprint of large-scale learning, by first summarizing a large dataset into a low-dimensional sketch vector, and then decoding from this sketch the latent…

Machine Learning · Computer Science 2024-06-18 Ayoub Belhadji , Rémi Gribonval

The streaming instability (SI) is a leading mechanism for planetesimal formation, driving the aerodynamic concentration of solids in protoplanetary disks. The SI triggers strong clumping (i.e., strong enough for clumps to collapse) when the…

Earth and Planetary Astrophysics · Physics 2025-09-24 Jeonghoon Lim , Jacob B. Simon , Rixin Li , Olivia Brouillette , David G. Rea , Wladimir Lyra

Natural materials often feature a combination of soft and stiff phases, arranged to achieve excellent mechanical properties, such as high strength and toughness. Many natural materials have even independently evolved to have similar…

Soft Condensed Matter · Physics 2025-07-03 Chelsea Fox , Kyrillos Bastawros , Tommaso Magrini , Chiara Daraio

As integrated circuits have become progressively more complex, constrained random stimulus has become ubiquitous as a means of stimulating a designs functionality and ensuring it fully meets expectations. In theory, random stimulus allows…

Machine Learning · Computer Science 2019-10-01 William Hughes , Sandeep Srinivasan , Rohit Suvarna , Maithilee Kulkarni

Density-driven segregations, extensively studied in a simple rotating drum, are enriched with a wide range of underlying physics. Diverse symmetrical segregation patterns formed by mixing two types of dry mono-sized grains have been…

Soft Condensed Matter · Physics 2025-02-21 Yu Chen , Deheng Wei , Si Suo , Mingrui Dong , Yixiang Gan

Dynamical clustering represents a characteristic feature of active matter consisting of self-propelled agents that convert energy from the environment into mechanical motion. At the micron scale, typical of overdamped dynamics, particles…

Soft Condensed Matter · Physics 2024-06-04 Lorenzo Caprini , Davide Breoni , Anton Ldov , Christian Scholz , Hartmut Löwen

Reactive flows in porous media play an important role in our life and are crucial for many industrial, environmental and biomedical applications. Very often the concentration of the species at the inlet is known, and the so-called…

Fluid Dynamics · Physics 2023-01-13 Daria Fokina , Pavel Toktaliev , Oleg Iliev , Ivan Oseledets

As granular materials flow and settle, interactions among particles of different sizes or properties drive mixing and segregation, producing rich dynamics that reshape systems ranging from industrial hoppers to planetary surfaces. A…

Soft Condensed Matter · Physics 2026-04-28 Hugo N. Ulloa , Tomás Trewhela

Fine particle suspensions (such as cornstarch mixed with water) exhibit dramatic changes in viscosity when sheared, producing fascinating behaviors that captivate children and rheologists alike. Recent examination of these mixtures in…

Soft Condensed Matter · Physics 2022-06-08 Aaron Baumgarten , Ken Kamrin

We combine experiments and numerical simulations to investigate the emergence of clogging in a system of interacting paramagnetic colloidal particles driven against a disordered landscape of larger obstacles. We consider a single aperture…

Soft Condensed Matter · Physics 2020-07-10 Sergi Granados Leyva , Ralph Lukas Stoop , Pietro Tierno , Ignacio Pagonabarraga

It is possible to build free-standing, load-bearing structures using only rocks and loops of elastic material. We investigate how these structures emerge, and find that the necessary maximum loop spacing (the critical spacing) is a function…

Soft Condensed Matter · Physics 2021-09-09 Arman Guerra , Douglas P. Holmes

Blockage of culverts by transported debris materials is reported as main contributor in originating urban flash floods. Conventional modelling approaches had no success in addressing the problem largely because of unavailability of peak…

Computer Vision and Pattern Recognition · Computer Science 2021-05-10 Umair Iqbal , Johan Barthelemy , Wanqing Li , Pascal Perez

Rate of grain growth, which aides in achieving desired properties in polycrystalline materials, is conventionally estimated by measuring the size of grains and tracking its change in micrographs reflecting the temporal evolution. Techniques…

Materials Science · Physics 2024-06-17 Manoj Prabakar , P G Kubendran Amos