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In this paper, we consider a stochastic system described by a differential equation admitting a spatially varying random coefficient. The differential equation has been employed to model various static physics systems such as elastic…

Probability · Mathematics 2013-09-18 Jingchen Liu , Xiang Zhou

Gaussian graphical models are widely utilized to infer and visualize networks of dependencies between continuous variables. However, inferring the graph is difficult when the sample size is small compared to the number of variables. To…

Statistics Theory · Mathematics 2016-09-30 Emilie Devijver , Mélina Gallopin

We study a stochastic differential equation driven by a gamma process, for which we give results on the existence of weak solutions under conditions on the volatility function. To that end we provide results on the density process between…

Probability · Mathematics 2023-10-18 Denis Belomestny , Shota Gugushvili , Moritz Schauer , Peter Spreij

Three ideas are introduced that when brought together characterize the realistic quasiclassical realms of our quantum universe as particular kinds of sets of alternative coarse-grained histories defined by quasiclassical variables: (1)…

Quantum Physics · Physics 2014-05-28 Murray Gell-Mann , James B. Hartle

Coupled length and time scales determine the dynamic behavior of polymers and underlie their unique viscoelastic properties. To resolve the long-time dynamics it is imperative to determine which time and length scales must be correctly…

Soft Condensed Matter · Physics 2016-02-16 K. Michael Salerno , Anupriya Agrawal , Dvora Perahia , Gary S. Grest

We introduce a concept of non-coherent evolution of macroscopic quantum systems. We show that for weakly interacting systems such evolution is a Markovian stochastic process. The transition rates between system states, which characterize…

Quantum Physics · Physics 2026-03-17 A. P. Meilakhs

Recently, a novel linear model predictive control algorithm based on a physics-informed Gaussian Process has been introduced, whose realizations strictly follow a system of underlying linear ordinary differential equations with constant…

Optimization and Control · Mathematics 2025-05-01 Adrian Lepp , Jörn Tebbe , Andreas Besginow

A broad class of stochastic volatility models are defined by systems of stochastic differential equations. While these models have seen widespread success in domains such as finance and statistical climatology, they typically lack an…

Machine Learning · Computer Science 2022-07-15 Gregory Benton , Wesley J. Maddox , Andrew Gordon Wilson

A formalism is presented to express decoherence both in the markovian and nonmarkovian regimes and both dissipative and nondissipative in isolated systems. The main physical hypothesis, already contained in the literature, amounts to…

Quantum Physics · Physics 2007-05-23 D. Salgado , J. L. Sanchez-Gomez

Conjugated organic molecules play a central role in a wide range of optoelectronic devices, including organic light-emitting diodes, organic field-effect transistors, and organic solar cells. A major bottleneck in the computational design…

We give an introductory account of the recently identified gauge invariance of the equilibrium statistical mechanics of classical many-body systems [J. M\"uller et al., Phys. Rev. Lett. Phys. Rev. Lett. 133, 217101 (2024)]. The gauge…

Statistical Mechanics · Physics 2025-03-26 Johanna Müller , Florian Sammüller , Matthias Schmidt

The principle of least action is one of the most fundamental physical principle. It says that among all possible motions connecting two points in a phase space, the system will exhibit those motions which extremise an action functional.…

Numerical Analysis · Mathematics 2022-10-17 Sina Ober-Blöbaum , Christian Offen

This paper proposes a physically consistent Gaussian Process (GP) enabling the identification of uncertain Lagrangian systems. The function space is tailored according to the energy components of the Lagrangian and the differential equation…

Machine Learning · Computer Science 2023-02-06 Giulio Evangelisti , Sandra Hirche

It is shown that the vacuum state of weakly interacting quantum field theories can be described, in the Heisenberg picture, as a linear combination of randomly distributed incoherent paths that obey classical equations of motion with…

High Energy Physics - Theory · Physics 2009-11-07 Ram Brustein , David H. Oaknin

Variational inequalities are an important mathematical tool for modelling free boundary problems that arise in different application areas. Due to the intricate nonsmooth structure of the resulting models, their analysis and optimization is…

Optimization and Control · Mathematics 2017-11-23 Juan-Carlos De Los Reyes

We give a geometric description of variational principles in mechanics, with special attention to constrained systems. For the general case of nonholonomic constraints, a unified variational approach is given, and the equations of motion of…

Mathematical Physics · Physics 2007-05-23 Xavier Gracia , Jesus Marin-Solano , Miguel-C. Munoz-Lecanda

The use of gyrokinetics, wherein phase-space coordinate transformations result in a phase-space dimensionality reduction as well as the removal of fast time scales, has enabled the simulation of microturbulence in fusion devices. The…

Plasma Physics · Physics 2025-09-10 Ronald Remmerswaal , Roman Hatzky , Eric Sonnendrücker

This article is concerned with the existence and the long time behavior of weak solutions to certain coupled systems of fourth-order degenerate parabolic equations of gradient flow type. The underlying metric is a Wasserstein-like…

Analysis of PDEs · Mathematics 2016-09-23 Daniel Matthes , Jonathan Zinsl

Granular materials are involved in most industrial and environmental processes, as well as many civil engineering applications. Although significant advances have been made in understanding the statics and dynamics of cohesionless grains…

Soft Condensed Matter · Physics 2025-11-21 Ram Sudhir Sharma , Alban Sauret

Multi-fidelity methods are prominently used when cheaply-obtained, but possibly biased and noisy, observations must be effectively combined with limited or expensive true data in order to construct reliable models. This arises in both…

Machine Learning · Statistics 2019-03-19 Kurt Cutajar , Mark Pullin , Andreas Damianou , Neil Lawrence , Javier González