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This paper gives a summary of basic concepts of density-functional theory (DFT) and its use in state-of-the-art computations of complex processes in condensed matter physics and materials science. In particular we discuss how microscopic…

Materials Science · Physics 2008-02-03 C. Ratsch , P. Ruggerone , M. Scheffler

We develop an analytic framework to understand fragmentation in turbulent, self-gravitating media. Previously, we showed some properties of turbulence can be predicted with the excursion-set formalism. Here, we generalize to fully…

Cosmology and Nongalactic Astrophysics · Physics 2013-07-02 Philip F. Hopkins

We develop a quenched thermodynamic formalism for a wide class of random maps with non-uniform expansion, where no Markov structure, no uniformly bounded degree or the existence of some expanding dynamics is required. We prove that every…

Dynamical Systems · Mathematics 2024-04-19 Manuel Stadlbauer , Shintaro Suzuki , Paulo Varandas

Assuming an effective quadratic Hamiltonian, we derive an approximate, linear stochastic equation of motion for the density-fluctuations in liquids, composed of overdamped Brownian particles. From this approach, time dependent two point…

Soft Condensed Matter · Physics 2017-04-26 Matthias Krüger , David S. Dean

We consider additive functionals of systems of random measures whose initial configuration is given by a Poisson point process, and whose individual components evolve according to arbitrary Markovian or non-Markovian measure valued…

Probability · Mathematics 2025-12-03 Arturo Jaramillo , Antonio Murillo-Salas

In this article, we investigate the convergence behavior of two classes of gathering protocols with fixed circulant topologies using tools from dynamical systems. Given a fixed number of mobile entities moving in the Euclidean plane, we…

Dynamical Systems · Mathematics 2026-04-15 Raphael Gerlach , Sören von der Gracht , Michael Dellnitz

We review a few representative examples of granular experiments or models where phase separation, accompanied by domain coarsening, is a relevant phenomenon. We first elucidate the intrinsic non-equilibrium, or athermal, nature of granular…

Statistical Mechanics · Physics 2015-04-29 Andrea Baldassarri , Andrea Puglisi , Alessandro Sarracino

In this paper, we discuss the spherically symmetric gravitational collapse of matter fields in the de Sitter universe. The energy-momentum tensor of the matter field is assumed to admit a wide variety including dust, perfect fluids with…

General Relativity and Quantum Cosmology · Physics 2026-05-15 Akriti Garg , Ayan Chatterjee

Non-spherical dynamical approximations and models for the gravitational collapse are used to extend the well-known Press \& Schechter (PS) approach, in order to determine analytical expressions for the mass function of cosmic structures.…

Astrophysics · Physics 2009-09-25 Pierluigi Monaco

We propose an exercise in which one attempts to deduce the formalism of quantum mechanics solely from phenomenological observations. The only assumed inputs are the multi-time probability distributions estimated from the results of…

In this paper we develop a formalism which models all massive particles as travelling at the speed of light(c). This is done by completing the 3-velocity v of a test particle to the speed of light by adding an auxiliary 3-velocity component…

General Relativity and Quantum Cosmology · Physics 2007-05-23 Burc Gokden

Many collective systems exist in nature far from equilibrium, ranging from cellular sheets up to flocks of birds. These systems reflect a form of active matter, whereby individual material components have internal energy. Under specific…

Soft Condensed Matter · Physics 2023-04-17 Namid R. Stillman , Silke Henkes , Roberto Mayor , Gilles Louppe

Sampling from unnormalized densities is analogous to the generative modeling problem, but the target distribution is defined by a known energy function instead of data samples. Because evaluating the energy function is often costly, a…

Machine Learning · Computer Science 2026-05-06 Aaron Havens , Brian Karrer , Neta Shaul

The intrinsic volumes induced by a stationary Poisson k-flat process inside a compact and convex sampling window are considered. Using techniques from stochastic analysis, more precisely calculus with multiple stochastic integrals and a…

Probability · Mathematics 2011-04-13 Matthias Schulte , Christoph Thaele

Gaussian process modeling is a standard tool for building emulators for computer experiments, which are usually used to study deterministic functions, for example, a solution to a given system of partial differential equations. This work…

Statistics Theory · Mathematics 2021-10-01 Wenjia Wang

We study the growth of large scale structure in two recently proposed non-standard cosmological models: the brane induced gravity model of Dvali, Gabadadze and Porrati (DGP) and the Cardassian models of Freese and Lewis. A general formalism…

Astrophysics · Physics 2009-11-07 T. Multamaki , E. Gaztanaga , M. Manera

We propose an interdisciplinary framework that combines Bayesian predictive inference, a well-established tool in Machine Learning, with Formal Methods rooted in the computer science community. Bayesian predictive inference allows for…

Computation · Statistics 2025-08-21 Laura Vana , Ennio Visconti , Laura Nenzi , Annalisa Cadonna , Gregor Kastner

Using hydrodynamic simulations we investigate the time evolution and fragmentation of regions within molecular clouds which have lost their turbulent support leading to gravitational contraction. The initial density distributions are…

Astrophysics · Physics 2009-10-31 Ralf Klessen , Andreas Burkert

We compute the effect of primordial non-Gaussianity on the halo mass function, using excursion set theory. In the presence of non-Gaussianity the stochastic evolution of the smoothed density field, as a function of the smoothing scale, is…

Cosmology and Nongalactic Astrophysics · Physics 2014-11-18 Michele Maggiore , Antonio Riotto

Diffusion Probabilistic Models stand as a critical tool in generative modelling, enabling the generation of complex data distributions. This family of generative models yields record-breaking performance in tasks such as image synthesis,…

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