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Shearing a disordered or amorphous solid for many cycles with a constant strain amplitude can anneal it, relaxing a sample to a steady state that encodes a memory of that amplitude. This steady state also features a remarkable stability to…

Soft Condensed Matter · Physics 2023-02-27 Nathan C. Keim , Dani Medina

We present a novel framework exploiting the cascade of phase transitions occurring during a simulated annealing of the Expectation-Maximisation algorithm to cluster datasets with multi-scale structures. Using the weighted local covariance,…

Machine Learning · Statistics 2021-01-13 T. Bonnaire , A. Decelle , N. Aghanim

Although many efficient heuristics have been developed to solve binary optimization problems, these typically produce correlated solutions for degenerate problems. Most notably, transverse-field quantum annealing - the heuristic employed in…

Disordered Systems and Neural Networks · Physics 2019-06-27 Zheng Zhu , Andrew J. Ochoa , Helmut G. Katzgraber

Binary population synthesis is the method by which predictions of varied observables of stellar populations can be made from theoretical models of binary stellar evolution. Binary stars have many more possible evolutionary outcomes compared…

Solar and Stellar Astrophysics · Physics 2020-06-15 J. J. Eldridge

As one of the most robust global optimization methods, simulated annealing has received considerable attention, with many variations that attempt to improve the cooling schedule. This paper introduces a variant of simulated annealing that…

Chemical Physics · Physics 2020-02-17 Mariia Karabin , Steven J. Stuart

We give a cross-disciplinary survey on ``population'' Monte Carlo algorithms. In these algorithms, a set of ``walkers'' or ``particles'' is used as a representation of a high-dimensional vector. The computation is carried out by a random…

Statistical Mechanics · Physics 2015-06-24 Yukito IBA

Simulated annealing is an effective and general means of optimization. It is in fact inspired by metallurgy, where the temperature of a material determines its behavior in thermodynamics. Likewise, in simulated annealing, the actions that…

Machine Learning · Computer Science 2020-07-01 Avrim Blum , Chen Dan , Saeed Seddighin

I simulate the evolution of a stellar system hosting two stellar populations whose initial set up is defined according to the two main scenarios proposed for the origin of multiple populations in Galactic globular clusters: (i) formation of…

Astrophysics of Galaxies · Physics 2021-02-17 A. Sollima

Numerical simulations of models and theories that describe complex systems such as spin glasses are becoming increasingly important. Beyond fundamental research, these computational methods also find practical applications in fields like…

Density tempering (also called density annealing) is a sequential Monte Carlo approach to Bayesian inference for general state models; it is an alternative to Markov chain Monte Carlo. When applied to state space models, it moves a…

Methodology · Statistics 2022-04-05 David Gunawan , Robert Kohn , Minh Ngoc Tran

The population distribution within the ground-state of an atomic ensemble is of large significance in a variety of quantum optics processes. We present a method to reconstruct the detailed population distribution from a set of absorption…

Quantum Physics · Physics 2010-06-04 P. London , O. Firstenberg , M. Shuker , A. Ron

We show that an analogy between crowding in fluid and jammed phases of hard spheres captures the density dependence of the kissing number for a family of numerically generated jammed states. We extend this analogy to jams of mixtures of…

The MIXANDMIX (mixtures by Anderson mixing) tool for the computation of the empirical spectral distribution of random matrices generated by mixtures of populations is described. Within the population mixture model the mapping between the…

Computation · Statistics 2019-06-21 Lucilio Cordero-Grande

Quantum annealing is a proposed combinatorial optimization technique meant to exploit quantum mechanical effects such as tunneling and entanglement. Real-world quantum annealing-based solvers require a combination of annealing and classical…

Quantum Physics · Physics 2015-07-30 Kenneth M. Zick , Omar Shehab , Matthew French

Normalizing flows can transform a simple prior probability distribution into a more complex target distribution. Here, we evaluate the ability and efficiency of generative machine learning methods to sample the Boltzmann distribution of an…

Soft Condensed Matter · Physics 2024-09-16 Gerhard Jung , Giulio Biroli , Ludovic Berthier

High-throughput computational screening of polymers offers a powerful way to address the imbalance between the vast number of polymers synthesised for diverse applications and the relatively small subset that can be studied using atomistic…

Materials Science · Physics 2026-03-12 Lois Smith , Samuel Ericson , Vittoria Fantauzzo , Chin Yong , Paola Carbone , Alessandro Troisi

Population Monte Carlo (PMC) sampling methods are powerful tools for approximating distributions of static unknowns given a set of observations. These methods are iterative in nature: at each step they generate samples from a proposal…

Computation · Statistics 2022-01-17 Víctor Elvira , Luca Martino , David Luengo , Mónica F. Bugallo

A framework is presented for carrying out simulations of equilibrium systems in the microcanonical ensemble using annealing in an energy ceiling. The framework encompasses an equilibrium version of simulated annealing, population annealing…

Statistical Mechanics · Physics 2019-12-18 Nathan Rose , Jonathan Machta

We systematically investigate the thermodynamic landscape of the 38-atom Lennard--Jones cluster LJ$_{38}$ using Population Annealing (PA), a method suited for systems with challenging double-funnel energy landscapes. By employing an…

Chemical Physics · Physics 2026-03-05 Akie Kowaguchi , Koji Hukushima

Exploiting quantum properties to outperform classical ways of information-processing is an outstanding goal of modern physics. A promising route is quantum simulation, which aims at implementing relevant and computationally hard problems in…