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The non-linear evolution of one-dimensional perturbations in a three-dimensional expanding Universe is considered. A general Lagrangian scheme is derived, and compared to two previously introduced approximate models. These models are…

Disordered Systems and Neural Networks · Physics 2007-05-23 Erik Aurell , Duccio Fanelli

Large scale numerical experiments are commonplace today in theoretical physics. The high performance algorithms described herein are the most compact, efficient methods known for representing and analyzing systems modeled well by sets or…

General Relativity and Quantum Cosmology · Physics 2018-05-14 William J. Cunningham

Motivated by the development of computer theory, the sorting algorithm is emerging in an endless stream. Inspired by decrease and conquer method, we propose a brand new sorting algorithmUltimately Heapsort. The algorithm consists of two…

Data Structures and Algorithms · Computer Science 2019-02-04 Feiyang Chen , Nan Chen , Hanyang Mao , Hanlin Hu

This paper deals with the problem of simulating dense dispersed systems composed by large numbers of particles undergoing ballistic aggregation. The most classical approaches for dealing with such problems are represented by the so-called…

Computational Physics · Physics 2024-09-25 Pierre Degond , Giacomo Dimarco , Marina Ferreira , Sophie Hecht

An explicit high-order noncanonical symplectic algorithm for ideal two-fluid systems is developed. The fluid is discretized as particles in the Lagrangian description, while the electromagnetic fields and internal energy are treated as…

Plasma Physics · Physics 2016-11-23 Jianyuan Xiao , Hong Qin , Philip J. Morrison , Jian Liu , Zhi Yu , Ruili Zhang , Yang He

A very simple heuristic approach to the unfolding problem will be described. An iterative algorithm starts with an empty histogram and every iteration aims to add one entry to this histogram. The entry to be added is selected according to a…

Data Analysis, Statistics and Probability · Physics 2014-11-06 Yordan Karadzhov

An activity fundamental to science is building mathematical models. These models are used to both predict the results of future experiments and gain insight into the structure of the system under study. We present an algorithm that…

Data Analysis, Statistics and Probability · Physics 2015-06-04 D. J. A. Hills , A. M. Grütter , J. J. Hudson

A large hadron machine like the LHC with its high track multiplicities always asks for powerful tools that drastically reduce the large background while selecting signal events efficiently. Actually such tools are widely needed and used in…

Data Analysis, Statistics and Probability · Physics 2014-11-20 Markward Britsch , Nikolai Gagunashvili , Michael Schmelling

Symplectic schemes are powerful methods for numerically integrating Hamiltonian systems, and their long-term accuracy and fidelity have been proved both theoretically and numerically. However direct applications of standard symplectic…

Plasma Physics · Physics 2019-06-26 Jianyuan Xiao , Hong Qin

We introduce an iterative method to search for time-optimal Hamiltonians that drive a quantum system between two arbitrary, and in general mixed, quantum states. The method is based on the idea of progressively improving the efficiency of…

Quantum Physics · Physics 2019-12-25 Francesco Campaioli , William Sloan , Kavan Modi , Felix Alexander Pollock

In this Thesis we develop the geometric formulations for higher-order autonomous and non-autonomous dynamical systems, and second-order field theories. In all cases, the physical information of the system is given in terms of a Lagrangian…

Mathematical Physics · Physics 2014-10-30 Pedro D. Prieto-Martínez

We introduce a new Lagrangian particle tracking algorithm that tracks particles in three dimensions to separations between trajectories approaching contact. The algorithm also detects low Weber number binary collisions that result in…

Fluid Dynamics · Physics 2020-07-15 Reece Vincent Kearney , Gregory Paul Bewley

By and large, Backpropagation (BP) is regarded as one of the most important neural computation algorithms at the basis of the progress in machine learning, including the recent advances in deep learning. However, its computational structure…

Artificial Intelligence · Computer Science 2018-08-22 Alessandro Betti , Marco Gori , Giuseppe Marra

We present an algorithm for finding the probabilities of rare events in nonequilibrium processes. The algorithm consists of evolving the system with a modified dynamics for which the required event occurs more frequently. By keeping track…

Statistical Mechanics · Physics 2011-04-07 Anupam Kundu , Sanjib Sabhapandit , Abhishek Dhar

The mathematical framework of hybrid system is a recent and general tool to treat control systems involving control action of heterogeneous nature. In this paper, we construct and test a semi-Lagrangian numerical scheme for solving the…

Numerical Analysis · Mathematics 2016-08-03 Roberto Ferretti , Achille Sassi

We investigate algorithmic control of a large swarm of mobile particles (such as robots, sensors, or building material) that move in a 2D workspace using a global input signal (such as gravity or a magnetic field). We show that a maze of…

Emerging Technologies · Computer Science 2017-12-05 Aaron T. Becker , Erik D. Demaine , Sándor P. Fekete , Jarrett Lonsforda , Rose Morris-Wright

This paper describes a fourth-order integration algorithm for the gravitational N-body problem based on discrete Lagrangian mechanics. When used with shared timesteps, the algorithm is momentum conserving and symplectic. We generalize the…

Astrophysics · Physics 2010-11-11 Will M. Farr , Edmund Bertschinger

In the paper we present results to develop an irreducible theory of complex systems in terms of self-organization processes of prime integer relations. Based on the integers and controlled by arithmetic only the self-organization processes…

Adaptation and Self-Organizing Systems · Physics 2007-05-23 Victor Korotkikh , Galina Korotkikh

We introduce a new high dimensional algorithm for efficiency corrected, maximally Monte Carlo event generator independent fiducial measurements at the LHC and beyond. The approach is driven probabilistically using a Deep Neural Network on…

Data Analysis, Statistics and Probability · Physics 2018-09-18 Mikael Mieskolainen

This paper presents a millisecond-level look-ahead control algorithm for energy storage with constant space complexity and worst-case linear run-time complexity. The algorithm connects the optimal control with the Lagrangian multiplier…

Optimization and Control · Mathematics 2019-12-13 Bolun Xu , Magnus Korpas , Audun Botterud , Francis O'Sullivan
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