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We formalize the intuitive idea of a labelled discrete surface which evolves in time, subject to two natural constraints: the evolution does not propagate information too fast; and it acts everywhere the same.

Discrete Mathematics · Computer Science 2014-04-02 Pablo Arrighi , Simon Martiel , Zizhu Wang

The task of learning patterns is typically associated with systems that update parameters on fixed architectures, such as neural networks, where learning proceeds through continuous optimization. Here, we demonstrate that pattern learning…

Disordered Systems and Neural Networks · Physics 2026-04-29 Shabeeb Ameen , Tao Zhang , J. M. Schwarz

Soft solids with tunable mechanical response are at the core of new material technologies, but a crucial limit for applications is their progressive aging over time, which dramatically affects their functionalities. The generally accepted…

Soft Condensed Matter · Physics 2017-08-02 Mehdi Bouzid , Jader Colombo , Lucas Vieira Barbosa , Emanuela Del Gado

We propose a model for rate-independent evolution in elastoplastic materials under external loading, which allows large strains. In the setting of strain-gradient plasticity with multiplicative decomposition of the deformation gradient, we…

Analysis of PDEs · Mathematics 2021-09-01 Martin Kružík , Jiří Zeman

We consider disordered solids in which the microscopic elements can deform plastically in response to stresses on them. We show that by driving the system periodically, this plasticity can be exploited to train in desired elastic…

Soft Condensed Matter · Physics 2022-06-01 Daniel Hexner , Andrea J. Liu , Sidney R. Nagel

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

Plasticity-led evolution is a form of evolution where a change in the environment induces novel traits via phenotypic plasticity, after which the novel traits are genetically accommodated over generations under the novel environment. This…

Populations and Evolution · Quantitative Biology 2023-01-18 Eden Tian Hwa Ng , Akira R. Kinjo

Disordered mechanical systems can deform along a network of pathways that branch and recombine at special configurations called bifurcation points. Multiple pathways are accessible from these bifurcation points; consequently, computer-aided…

Soft Condensed Matter · Physics 2023-02-22 Chukwunonso Arinze , Menachem Stern , Sidney R. Nagel , Arvind Murugan

Biological information processing manifests a huge variety in its complexity and capability among different organisms, which presumably stems from the evolutionary optimization under limited computational resources. Starting from the…

Biological Physics · Physics 2025-10-21 Takehiro Tottori , Tetsuya J. Kobayashi

A phenomenon recently coined as ``overaging'' implies a slowdown in the collective (slow) relaxation modes of a glass when a transient shear strain is imposed. We are able to reproduce this behavior in simulations of a supercooled polymer…

Soft Condensed Matter · Physics 2009-11-11 Matthew L. Wallace , Bela Joos

As Evolutionary Dynamics moves from the realm of theory into application, algorithms are needed to move beyond simple models. Yet few such methods exist in the literature. Ecological and physiological factors are known to be central to…

Populations and Evolution · Quantitative Biology 2025-05-20 Bryce Allen Bagley , Navin Khoshnan , Claudia K Petritsch

Aging is a fundamental aspect of living systems that undergo a progressive deterioration of physiological function with age and an increase of vulnerability to disease and death. Living systems, known as complex systems, require complexity…

Populations and Evolution · Quantitative Biology 2010-11-15 Byung Mook Weon , Jung Ho Je

As artificial intelligence systems (AIs) become increasingly produced by recursive self-improvement, a form of evolution may emerge, with the traits of AI systems shaped by the success of earlier AIs in designing and propagating their…

Artificial Intelligence · Computer Science 2026-04-14 Kenneth D Harris

Many systems in nature, glasses, interfaces and fractures being some examples, cannot equilibrate with their environment, which gives rise to novel and surprising behaviour such as memory effects, ageing and nonlinear dynamics. Unlike their…

Superconductivity · Physics 2015-05-13 Xu Du , Guohong Li , Eva Y. Andrei , M. Greenblatt , P. Shuk

The electrical resistance decay of a metallic granular packing has been measured as a function of time. This measurement gives information about the size of the conducting cluster formed by the well connected grains. Several regimes have…

Materials Science · Physics 2009-11-10 S. Dorbolo , M. Ausloos , N. Vandewalle , M. Houssa

Many living and artificial systems improve their fitness or performance by adapting to changing environments or diverse training data. However, it remains unclear how such environmental variation influences adaptation, what is learned in…

Computational Physics · Physics 2026-04-09 Mengjie Zu , Carl P. Goodrich

A variety of complex fluids consist in soft, round objects (foams, emulsions, assemblies of copolymer micelles or of multilamellar vesicles -- also known as onions). Their dense packing induces a slight deviation from their prefered…

Soft Condensed Matter · Physics 2009-11-13 Sylvain Bénito , Charles-Henri Bruneau , Thierry Colin , Cyprien Gay , François Molino

Regardless of a system's complexity or scale, its growth can be considered to be a spontaneous thermodynamic response to a local convergence of down-gradient material flows. Here it is shown how growth can be constrained to a few distinct…

Atmospheric and Oceanic Physics · Physics 2012-11-14 Timothy J. Garrett

Auxetic materials, or negative-Poisson's-ratio materials, are important technologically and fascinating theoretically. When loaded by external stresses, their internal strains are governed by correlated motion of internal structural degrees…

Materials Science · Physics 2012-04-16 Raphael Blumenfeld , Sam F. Edwards

We show that neural networks trained by evolutionary reinforcement learning can enact efficient molecular self-assembly protocols. Presented with molecular simulation trajectories, networks learn to change temperature and chemical potential…

Statistical Mechanics · Physics 2020-06-01 Stephen Whitelam , Isaac Tamblyn