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Related papers: Computer Simulations of Causal Sets

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In the classical world, physical events always happen in a fixed causal order. However, it was recently revealed that quantum mechanics allows events to occur with indefinite causal order (ICO). In this study, we use an optical quantum…

Causal inference is a fundamental research topic for discovering the cause-effect relationships in many disciplines. However, not all algorithms are equally well-suited for a given dataset. For instance, some approaches may only be able to…

Machine Learning · Computer Science 2024-03-11 Zhipeng Ma , Marco Kemmerling , Daniel Buschmann , Chrismarie Enslin , Daniel Lütticke , Robert H. Schmitt

Video-based human pose estimation has long been a fundamental yet challenging problem in computer vision. Previous studies focus on spatio-temporal modeling through the enhancement of architecture design and optimization strategies.…

Computer Vision and Pattern Recognition · Computer Science 2025-01-27 Haipeng Chen , Sifan Wu , Zhigang Wang , Yifang Yin , Yingying Jiao , Yingda Lyu , Zhenguang Liu

The conformal covariance of correlation functions is checked in the second-order transition induced by random bonds in the two-dimensional 8-state Potts model. The decay of correlations is obtained {\it via} transfer matrix calculations in…

Statistical Mechanics · Physics 2009-10-31 Christophe Chatelain , Bertrand Berche

This article is a tutorial on Markov chain Monte Carlo simulations and their statistical analysis. The theoretical concepts are illustrated through many numerical assignments from the author's book on the subject. Computer code (in Fortran)…

Statistical Mechanics · Physics 2016-08-31 Bernd A. Berg

Most traditional models of uncertainty have focused on the associational relationship among variables as captured by conditional dependence. In order to successfully manage intelligent systems for decision making, however, we must be able…

Artificial Intelligence · Computer Science 2015-05-19 David Heckerman , Ross D. Shachter

Classical machine learning techniques often struggle with overfitting and unreliable predictions when exposed to novel conditions. Introducing causality into the modelling process offers a promising way to mitigate these challenges by…

Computational Engineering, Finance, and Science · Computer Science 2025-05-28 David Zapata Gonzalez , Marcel Meyer , Oliver Mueller

We present a method for simulating fluid vesicles with in-plane orientational ordering. The method involves computation of local curvature tensor and parallel transport of the orientational field on a randomly triangulated surface. It is…

Biological Physics · Physics 2015-05-18 N. Ramakrishnan , P. B. Sunil Kumar , John H. Ipsen

We present Monte Carlo simulations of surface induced disordering in a model of a binary alloy on a bcc lattice which undergoes a first order bulk transition from the ordered DO3 phase to the disordered A2 phase. The data are analyzed in…

Statistical Mechanics · Physics 2009-10-31 F. F. Haas , F. Schmid , K. Binder

The Causal Set approach to quantum gravity asserts that spacetime, at its smallest length scale, has a discrete structure. This discrete structure takes the form of a locally finite order relation, where the order, corresponding with the…

General Relativity and Quantum Cosmology · Physics 2007-05-23 David Rideout

We investigate the continuum q-Potts model at its transition point from the disordered to the ordered regime, with particular emphasis on the coexistence of disordered and ordered phases in the high-q case. We argue that occurrence of phase…

Mathematical Physics · Physics 2007-05-23 Hans-Otto Georgii , Jozsef Lorinczi , Jani M. Lukkarinen

We introduce a new matrix model that describes Causal Dynamical Triangulations (CDT) in two dimensions. In order to do so, we introduce a new, simpler definition of 2D CDT and show it to be equivalent to the old one. The model makes use of…

High Energy Physics - Theory · Physics 2009-06-28 Dario Benedetti , Joe Henson

Controlling phase transitions in quantum systems via coupling to reservoirs has been mostly studied for idealized memory-less environments under the so-called Markov approximation. Yet, most quantum materials and experiments in the solid…

Quantum Physics · Physics 2024-10-11 Baptiste Debecker , John Martin , François Damanet

Computer simulators are nowadays widely used to understand complex physical systems in many areas such as aerospace, renewable energy, climate modeling, and manufacturing. One fundamental issue in the study of computer simulators is known…

Methodology · Statistics 2019-02-05 Feng Yang , C. Devon Lin , Pritam Ranjan

Condensed matter physics plays a crucial role in modern scientific research and technological advancements, providing insights into the behavior of materials and their fundamental properties. Understanding complex phenomena and systems in…

Quantum Physics · Physics 2023-08-03 Muhammad Arsalan Ali

A range of percolation models of cluster systems of composites is discussed. In the models the parameters of the clusters of a substance and inner boundaries were obtained by the Monte Carlo method, and the possibility of affecting the…

Materials Science · Physics 2017-08-18 Alexander Herega

We provide a numerical study of the macroscopic model of [3] derived from an agent-based model for a system of particles interacting through a dynamical network of links. Assuming that the network remodelling process is very fast, the…

We use an efficient method that eases the daunting task of simulating dynamics in spin systems with long-range interaction. Our Monte Carlo simulations of the long-range Ising model for the nonequilibrium phase ordering dynamics in two…

Statistical Mechanics · Physics 2019-01-23 Henrik Christiansen , Suman Majumder , Wolfhard Janke

We reformulate Pearl's three rules of do-calculus in the language of completely positive (CP) trace-preserving maps, thereby extending them to quantum systems with entanglement. We prove that Rule~2 fails whenever the underlying process…

Quantum Physics · Physics 2025-08-08 Jordi Vallverdu

Computational mechanics, an approach to structural complexity, defines a process's causal states and gives a procedure for finding them. We show that the causal-state representation--an $\epsilon$-machine--is the minimal one consistent with…

Statistical Mechanics · Physics 2022-02-17 Cosma Rohilla Shalizi , James P. Crutchfield