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Stochastic multi-scale modeling and simulation for nonlinear thermo-mechanical problems of composite materials with complicated random microstructures remains a challenging issue. In this paper, we develop a novel statistical higher-order…

Numerical Analysis · Mathematics 2023-08-23 Hao Dong , Junzhi Cui

We perform large scale simulations to characterize the transition in quenched QCD. It is shown by a rigorous finite size scaling that the transition is of first order. After this qualitative feature quantitative results are obtained with…

High Energy Physics - Lattice · Physics 2022-04-19 S. Borsanyi , Z. Fodor , D. A. Godzieba , R. Kara , P. Parotto , D. Sexty

The preparation of thermal states of matter is a crucial task in quantum simulation. In this work, we prove that a recently introduced, efficiently implementable dissipative evolution thermalizes to the Gibbs state in time scaling…

Quantum Physics · Physics 2026-04-20 Cambyse Rouzé , Daniel Stilck França , Álvaro M. Alhambra

We perform molecular dynamics simulations of repulsive athermal systems sheared at fixed normal load to study the effective temperature $T_L$ defined from time-dependent fluctuation-dissipation relations for density. We show that these…

Soft Condensed Matter · Physics 2009-11-10 Ning Xu , Corey S. O'Hern

We propose an iterative variational quantum algorithm to simulate the time evolution of arbitrary initial states within a given subspace. The algorithm compresses the Trotter circuit into a shorter-depth parameterized circuit, which is…

Quantum Physics · Physics 2026-02-24 Seung Park , Dongkeun Lee , Jeongho Bang , Hoon Ryu , Kyunghyun Baek

It is now well known that neural networks can be wrong with high confidence in their predictions, leading to poor calibration. The most common post-hoc approach to compensate for this is to perform temperature scaling, which adjusts the…

Computer Vision and Pattern Recognition · Computer Science 2022-07-25 Tom Joy , Francesco Pinto , Ser-Nam Lim , Philip H. S. Torr , Puneet K. Dokania

This work proposes an adaptive sequential Monte Carlo sampling algorithm to solve Bayesian inverse problems in scenarios where likelihood evaluations are costly but can be approximated using a surrogate model built from previous evaluations…

Computation · Statistics 2025-08-26 Frederic Cerou , Patrick Heas , Mathias Rousset

Diversity is an essential metric for evaluating the creativity of outputs generated by language models. Temperature-based sampling is a common strategy to increase diversity. However, for tasks that require high precision, e.g.,…

Machine Learning · Computer Science 2025-10-03 Sergey Troshin , Wafaa Mohammed , Yan Meng , Christof Monz , Antske Fokkens , Vlad Niculae

We study the Intermediate Scattering Function (ISF) of the strongly-nonlinear Fermi-Pasta Ulam Model at thermal equilibrium, using both numerical and analytical methods. From the molecular dynamics simulations we distinguish two limit…

Chaotic Dynamics · Physics 2019-01-30 Graziano Amati , Hugues Meyer , Tanja Schilling

Accurate and reliable state of charge (SoC) estimation becomes increasingly important to provide a stable and efficient environment for Lithium-ion batteries (LiBs) powered devices. Most data-driven SoC models are built for a fixed ambient…

Machine Learning · Computer Science 2021-01-12 Yan Qin , Stefan Adams , Chau Yuen

Markov Chain Monte Carlo (MCMC) underlies both statistical physics and combinatorial optimization, but mixes slowly near critical points and in rough landscapes. Parallel Tempering (PT) improves mixing by swapping replicas across…

Machine Learning · Computer Science 2025-09-30 Saleh Bunaiyan , Corentin Delacour , Shuvro Chowdhury , Kyle Lee , Kerem Y. Camsari

The tempered Lefschetz thimble method is a parallel-tempering algorithm towards solving the numerical sign problem. It uses the flow time of the gradient flow as a tempering parameter and is expected to tame both the sign and multimodal…

Strongly Correlated Electrons · Physics 2019-12-25 Masafumi Fukuma , Nobuyuki Matsumoto , Naoya Umeda

We introduce a tempering approach with stochastic density functional theory (sDFT), labeled t-sDFT, which reduces the statistical errors in the estimates of observable expectation values. This is achieved by rewriting the electronic density…

Computational Physics · Physics 2021-12-15 Minh Nguyen , Wenfei Li , Yangtao Li , Roi Baer , Eran Rabani , Daniel Neuhauser

Innovative numerical scheme studied in this work enables to overcome two main limitations of Building Performance Simulation (BPS) programs as high computational cost and the choice of a very fine numerical grid. The method, called…

Computational Engineering, Finance, and Science · Computer Science 2020-01-07 Madina Abdykarim , Julien Berger , Denys Dutykh , Amen Agbossou

After having developed a method that measures real time evolution of quantum systems at a finite temperature, we present here the simplest field theory where this scheme can be applied to, namely the 1+1 Ising model. We will compute the…

High Energy Physics - Theory · Physics 2016-09-06 E. Mendel

Ultra-fast electronic phenomena originating from finite temperature, such as nonlinear optical excitation, can be simulated with high fidelity via real-time time dependent density functional theory (rt-TDDFT) calculations with hybrid…

Materials Science · Physics 2025-01-07 Rongrong Liu , Zhuoqiang Guo , Qiuchen Sha , Tong Zhao , Haibo Li , Wei Hu , Lijun Liu , Guangming Tan , Weile Jia

Physical systems driven away from equilibrium by an external controller dissipate heat to the environment; the excess entropy production in the thermal reservoir can be interpreted as a "cost" to transform the system in a finite time. The…

Statistical Mechanics · Physics 2024-12-31 Jérémie Klinger , Grant M. Rotskoff

This note introduces a method for sampling Ising models with mixed boundary conditions. As an application of annealed importance sampling and the Swendsen-Wang algorithm, the method adopts a sequence of intermediate distributions that keeps…

Numerical Analysis · Mathematics 2022-05-19 Lexing Ying

Switching dynamical systems provide a powerful, interpretable modeling framework for inference in time-series data in, e.g., the natural sciences or engineering applications. Since many areas, such as biology or discrete-event systems, are…

Machine Learning · Computer Science 2021-09-30 Lukas Köhs , Bastian Alt , Heinz Koeppl

A densely packed granular system is an example of an out-of-equilibrium system in the jammed state. It has been a longstanding problem to determine whether this class of systems can be described by concepts arising from equilibrium…

Disordered Systems and Neural Networks · Physics 2009-11-11 Chaoming Song , Ping Wang , Hernan A. Makse