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The familiar theories of physics have the feature that the application of the theory to make predictions in specific circumstances can be done by means of an algorithm. We propose a more precise formulation of this feature --- one based on…

General Relativity and Quantum Cosmology · Physics 2018-06-26 Robert Geroch , James B. Hartle

It is a fascinating subject to explore how well we can understand the processes of life on the basis of fundamental laws of physics. It is emphasised that viewing biological processes as manipulation of information extracts their essential…

Quantum Physics · Physics 2007-05-23 Apoorva Patel

We develop a non-perturbative approach to simulating scattering on classical and quantum computers, in which the initial and final states contain a fixed number of composite particles. The construction is designed to mimic a particle…

Quantum Physics · Physics 2023-10-30 Michael Kreshchuk , James P. Vary , Peter J. Love

I give a highly selective overview of the way statistical mechanics explains the microscopic origins of the time asymmetric evolution of macroscopic systems towards equilibrium and of first order phase transitions in equilibrium. These…

Mathematical Physics · Physics 2009-10-31 Joel L. Lebowitz

Motivated by a general principle governing regulation mechanisms in biological cells, we investigate a general interaction scheme between different populations of particles and specific particles, referred to as agents. Assuming that each…

Probability · Mathematics 2023-10-10 Vincent Fromion , Philippe Robert , Jana Zaherddine

Recent developments in the mathematical foundations of quantum mechanics have brought the theory closer to that of classical probability and statistics. On the other hand, the unique character of quantum physics sets many of the questions…

Quantum Physics · Physics 2007-05-23 O. E. Barndorff-Nielsen , R. D. Gill , P. E. Jupp

Newtonian dynamics is derived from prior information codified into an appropriate statistical model. The basic assumption is that there is an irreducible uncertainty in the location of particles so that the state of a particle is defined by…

Classical Physics · Physics 2009-05-27 Ariel Caticha , Carlo Cafaro

The style of mathematical models known to probabilists as Interacting Particle Systems and exemplified by the Voter, Exclusion and Contact processes have found use in many academic disciplines. In many such disciplines the underlying…

Statistics Theory · Mathematics 2013-09-27 David Aldous

We study the motion of an inertial particle in a fractional Gaussian random field. The motion of the particle is described by Newton's second law, where the force is proportional to the difference between a background fluid velocity and the…

Dynamical Systems · Mathematics 2012-03-20 Georg Schöchtel

Algorithmic statistics considers the following problem: given a binary string $x$ (e.g., some experimental data), find a "good" explanation of this data. It uses algorithmic information theory to define formally what is a good explanation.…

Machine Learning · Computer Science 2015-09-21 Alexey Milovanov

Studying systems where many individual bodies in motion interact with one another is a complex and interesting area. Simple mechanisms that may be determined for biological, chemical, or physical reasons can lead to astonishingly complex…

Quantitative Methods · Quantitative Biology 2023-01-03 Cameron McNamee , Renee Reijo Pera

From behavioral sciences to biology to quantum mechanics, one encounters situations where (i) a system outputs several random variables in response to several inputs, (ii) for each of these responses only some of the inputs may "directly"…

Probability · Mathematics 2015-09-01 E. N. Dzhafarov , J. V. Kujala

Physicists have, hitherto, mostly adopted a frequentist conception of probability, according to which probability statements apply only to ensembles. It is argued that we should, instead, adopt an epistemic, or Bayesian conception, in which…

Quantum Physics · Physics 2009-11-10 D. M. Appleby

In the last quarter of the nineteenth century, Ludwig Boltzmann explained how irreversible macroscopic laws, in particular the second law of thermodynamics, originate in the time-reversible laws of microscopic physics. Boltzmann's analysis,…

Statistical Mechanics · Physics 2007-05-23 Sheldon Goldstein

We propose a Statistical-Mechanics inspired framework for modeling economic systems. Each agent composing the economic system is characterized by a few variables of distinct nature (e.g. saving ratio, expectations, etc.). The agents…

Statistical Mechanics · Physics 2007-05-23 Tom Erez , Martin Hohnisch , Sorin Solomon

The aim of this work is to show that particle mechanics, both classical and quantum, Hamiltonian and Lagrangian, can be derived from few simple physical assumptions. Assuming deterministic and reversible time evolution will give us a…

Classical Physics · Physics 2018-07-26 Gabriele Carcassi , Christine A. Aidala , David J. Baker , Lydia Bieri

Physical processes are computations only when we use them to externalize thought. Computation is the performance of one or more fixed processes within a contingent environment. We reformulate the Church-Turing thesis so that it applies to…

Multiagent Systems · Computer Science 2007-05-23 Russ Abbott

We analyze statistical consequences of a conjecture that there exists a fundamental (indivisible) quant of time. We study particle dynamics with discrete time. We show that a quantum-like interference pattern could appear as a statistical…

Quantum Physics · Physics 2007-05-23 Andrei Khrennikov , Yaroslav Volovich

Optical simulators for the Ising model have demonstrated great promise for solving challenging problems in physics and beyond. Here, we develop a spatial optical simulator for a variety of classical statistical systems, including the clock,…

Optics · Physics 2024-12-19 Song-Tao Yu , Ming-Gen He , Sheng Fang , Youjin Deng , Zhen-Sheng Yuan

Many stochastic physical systems evolve smoothly over time in the sense that the distribution of states changes regularly across time steps. The transition from current state to the next state can often be modeled as the combination of a…

Machine Learning · Computer Science 2026-05-29 Jules Berman , Tobias Blickhan , Benjamin Peherstorfer