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Related papers: Probabilistic interpretation of mechanical motion

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We propose a new interpretation of objective deterministic chances in statistical physics based on physical computational complexity. This notion applies to a single physical system (be it an experimental set--up in the lab, or a subsystem…

Quantum Physics · Physics 2013-03-20 Amit Hagar , Giuseppe Sergioli

We study the process of observation (measurement), within the framework of a `perspectival' (`relational', `relative state') version of the modal interpretation of quantum mechanics. We show that if we assume certain features of…

Quantum Physics · Physics 2007-05-23 Gyula Bene , Dennis Dieks

We introduce a cycle-expansion (fully deterministic) technique to compute the asymptotic behavior of arbitrary order transport moments. The theory is applied to different kinds of one-dimensional intermittent maps, and Lorentz gas with…

Chaotic Dynamics · Physics 2009-11-10 R. Artuso , G. Cristadoro

We develop a theory for the temporal integration of visual motion motivated by psychophysical experiments. The theory proposes that input data are temporally grouped and used to predict and estimate the motion flows in the image sequence.…

Computer Vision and Pattern Recognition · Computer Science 2012-01-06 Pierre-Yves Burgi , Alan L. Yuille , Norberto M. Grzywacz

Basic principles of mathematical modeling are reviewed in this book, with the focus on physics and its practical applications, and examples of selected mathematical methods are presented. Most of the models have been imported from physics…

Classical Physics · Physics 2025-07-14 Sergej Pankratow

The approach to the theory of a relativistic random process is considered by the path integral method as Brownian motion taking into account the boundedness of speed. An attempt was made to build a relativistic analogue of the Wiener…

General Relativity and Quantum Cosmology · Physics 2024-05-30 E. A. Kurianovich , A. I. Mikhailov , I. V. Volovich

This paper presents a probabilistic approach to represent and quantify model-form uncertainties in the reduced-order modeling of complex systems using operator inference techniques. Such uncertainties can arise in the selection of an…

Machine Learning · Statistics 2024-11-08 Jin Yi Yong , Rudy Geelen , Johann Guilleminot

The role of probability in quantum mechanics is reviewed, with a discussion of the ``orthodox'' versus the statistical interpretive frameworks, and of a number of related issues. After a brief summary of sources of unease with quantum…

Quantum Physics · Physics 2007-05-23 Stephen L. Adler

This is a general description of a probabilistic formalism of mechanics, i.e., an extension of the Newtonian mechanics principles to the systems undergoing random motion. From an analysis of the induction procedure from experimental data to…

Statistical Mechanics · Physics 2010-03-29 Qiuping A. Wang

Biological function of living matter is fulfilled by complex motions of biological and soft matter. Unlike general motion is deterministic described by Newton's laws, these motions are mostly random and uncertain for the position in…

Soft Condensed Matter · Physics 2026-04-28 Jun Ma

Motion of particles (bodies) in presence of random effects can be considered stochastic process. However, application of widely known stochastic processes used for description of particle motion is reduced to relatively small class of…

General Physics · Physics 2022-04-14 Jiri Prochazka

The classical behaviour of a macroscopic system consisting of a large number of microscopic systems is derived in the framework of the Bohmian interpretation of quantum mechanics. Under appropriate assumptions concerning the localization…

Quantum Physics · Physics 2016-09-08 H. Geiger , G. Obermair , Ch. Helm

Probabilistic inference provides a language for describing how organisms may learn from and adapt to their environment. The computations needed to implement probabilistic inference often require specific representations, akin to having the…

Molecular Networks · Quantitative Biology 2018-06-28 Yarden Katz , Michael Springer , Walter Fontana

We propose an original approach to describe the scientific progress in a quantitative way. Using innovative Machine Learning techniques we create a vector representation for the PACS codes and we use them to represent the relative movements…

Physics and Society · Physics 2020-07-01 Andrea Palmucci , Hao Liao , Andrea Napoletano , Andrea Zaccaria

In this paper, I propose a realistic interpretation (RI) of quantum mechanics, that is, an interpretation according to which a particle follows a definite path in spacetime. The path is not deterministic but it is rather a random walk.…

Cellular Automata and Lattice Gases · Physics 2007-09-18 Antonio Sciarretta

Higher-derivative analogues of multidimensional conformal particle and many-body conformal mechanics are constructed. Their Newton-Hooke counterparts are derived by applying appropriate coordinate transformations.

High Energy Physics - Theory · Physics 2017-10-03 Oleg Baranovsky

Gyroscopic motion explanation in texts is relatively long and requires reasonable level of comfort with the mathematical tools used. On the other hand, popular explanation outside academic courses does not explain the phenomenon and only…

Physics Education · Physics 2022-01-04 M. N. Tarabishy

The motion of overdamped particles in a one-dimensional spatially-periodic potential is considered. The potential is also randomly-fluctuating in time, due to multiplicative colored noise terms, and has a deterministic tilt. Numerical…

Statistical Mechanics · Physics 2013-06-06 James P. Gleeson

We suggest how to give a physical interpretation of Stochastic Loewner Evolution traces approaching a marked point in the upper half plane. We show that this may be related to the fusion of boundary with bulk fields in Conformal Field…

High Energy Physics - Theory · Physics 2011-11-10 Annekathrin Müller-Lohmann

Probabilistic representations of movement primitives open important new possibilities for machine learning in robotics. These representations are able to capture the variability of the demonstrations from a teacher as a probability…

Machine Learning · Computer Science 2020-02-20 Sebastian Gomez-Gonzalez , Gerhard Neumann , Bernhard Schölkopf , Jan Peters
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