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We review some recent methods of subgrid-scale parameterization used in the context of climate modeling. These methods are developed to take into account (subgrid) processes playing an important role in the correct representation of the…

Statistical Mechanics · Physics 2017-01-18 Jonathan Demaeyer , Stéphane Vannitsem

An approach to the mechanical behaviour of textile composites at the scale of their constituting fibres, using an implicit finite element simulation code, is proposed in this chapter. The approach is based on efficient methods and…

General Physics · Physics 2012-01-24 Damien Durville

A design for a pump is suggested which is based on well-known peristaltic pumps. In order to simply describe the operation of the proposed pump, an innovative interpretation of low Reynolds number swimmers is presented and thereafter a…

Soft Condensed Matter · Physics 2013-08-06 Farnoush Farahpour , Mohammad Reza Ejtehadi

Computing long-timescale kinetics of biomolecular processes remains a major challenge for atomistic simulations. A way out is to exploit local kinetic information to construct the global stationary flux across the reaction space. The…

Chemical Physics · Physics 2026-05-19 Ru Wang , Xiaojun Ji , Hao Wang , Wenjian Liu

This paper presents a novel method for introducing time into discrete and continuous spatial representations used in mobile robotics, by modelling long-term, pseudo-periodic variations caused by human activities. Unlike previous approaches,…

Robotics · Computer Science 2026-03-16 Tomas Krajnik , Tomas Vintr , Sergi Molina , Jaime P. Fentanes , Grzegorz Cielniak , Tom Duckett

Coupling is a widely used technique in the theoretical study of interacting stochastic processes. In this paper I present an example demonstrating its usefulness also in the efficient computer simulation of such processes. I first describe…

Populations and Evolution · Quantitative Biology 2012-04-16 Ilmari Karonen

Dynamical systems with a distributed yet interconnected structure, like multi-rigid-body robots or large-scale multi-agent systems, introduce valuable sparsity into the system dynamics that can be exploited in an optimal control setting for…

Robotics · Computer Science 2021-04-08 Roshan Pradhan , Shuo Yang , Frank Dellaert , Howie Choset , Matthew Travers

Optimal prediction approximates the average solution of a large system of ordinary differential equations by a smaller system. We present how optimal prediction can be applied to a typical problem in the field of molecular dynamics, in…

Mathematical Physics · Physics 2008-11-15 Benjamin Seibold

Mesoscale simulations of woven composites using parameterized analytical geometries offer a way to connect constituent material properties and their geometric arrangement to effective composite properties and performance. However, the…

Materials Science · Physics 2023-02-21 Collin W. Foster , Lincoln N. Collins , Francesco Panerai , Scott A. Roberts

We are concerned with the simulation and optimization of large-scale gas pipeline systems in an error-controlled environment. The gas flow dynamics is locally approximated by sufficiently accurate physical models taken from a hierarchy of…

Optimization and Control · Mathematics 2021-11-10 Pia Domschke , Oliver Kolb , Jens Lang

Simulations at the atomic scale provide a direct and effective way to understand the mechanical properties of materials. In the regime of classical mechanics, simulations for the thermodynamic properties of metals and alloys can be done by…

Computational Physics · Physics 2019-11-05 Ka-Ming Tam , Nicholas Walker , Samuel Kellar , Mark Jarrell

Atomistic simulations using accurate energy functions can provide molecular-level insight into functional motions of molecules in the gas- and in the condensed phase. Together with recently developed and currently pursued efforts in…

Chemical Physics · Physics 2022-01-12 M. Meuwly

Understanding molecular structure, dynamics, and reactivity requires bridging processes that occur across widely separated time scales. Conventional molecular dynamics simulations provide atomistic resolution, but their femtosecond time…

Chemical Physics · Physics 2025-10-10 Juan Viguera Diez , Mathias Schreiner , Simon Olsson

Metropolis Monte Carlo simulation is a powerful tool for studying the equilibrium properties of matter. In complex condensed-phase systems, however, it is difficult to design Monte Carlo moves with high acceptance probabilities that also…

Statistical Mechanics · Physics 2014-05-27 Jerome P. Nilmeier , Gavin E. Crooks , David D. L. Minh , John D. Chodera

Gaussian processes (GPs) are commonplace in spatial statistics. Although many non-stationary models have been developed, there is arguably a lack of flexibility compared to equipping each location with its own parameters. However, the…

Machine Learning · Statistics 2018-07-19 Leo L. Duan , Xia Wang , Rhonda D. Szczesniak

Thermostatically controlled loads such as refrigerators are exceptionally suitable as a flexible demand resource. This paper derives a decentralised load control algorithm for refrigerators. It is adapted from an existing continuous time…

Systems and Control · Electrical Eng. & Systems 2020-11-03 Simon H. Tindemans , Goran Strbac

Climate models are limited by heavy computational costs, often producing outputs at coarse spatial resolutions, while many climate change impact studies require finer scales. Statistical downscaling bridges this gap, and we adapt the…

Machine Learning · Computer Science 2025-11-06 Maryam Alipourhajiagha , Pierre-Louis Lemaire , Youssef Diouane , Julie Carreau

A consistent description of simultaneous heat and particle transport, including cross effects, and the associated entropy balance is given in the framework of a deterministic dynamical system. This is achieved by a multibaker map where,…

chao-dyn · Physics 2009-10-31 Laszlo Matyas , Tamas Tel , Jurgen Vollmer

The growing complexity of the power grid, driven by increasing share of distributed energy resources and by massive deployment of intelligent internet-connected devices, requires new modelling tools for planning and operation. Physics-based…

Machine Learning · Statistics 2018-11-26 Francesco Fusco

Many important chemical and biochemical processes in the condensed phase are notoriously difficult to simulate numerically. Often this difficulty arises from the complexity of simulating dynamics resulting from coupling to structured,…