English
Related papers

Related papers: Why neural functionals suit statistical mechanics

200 papers

We study Density Functional Theory models for systems which are translationally invariant in some directions, such as a homogeneous 2-d slab in the 3-d space. We show how the different terms of the energy are modified and we derive reduced…

Mathematical Physics · Physics 2021-12-24 David Gontier , Salma Lahbabi , Abdallah Maichine

Finite temperature density functional theory provides, in principle, an exact description of the thermodynamical equilibrium of many-electron systems. In practical applications, however, the functionals must be approximated. Efficient and…

Statistical Mechanics · Physics 2015-05-19 S. Pittalis , C. R. Proetto , A. Floris , A. Sanna , C. Bersier , K. Burke , E. K. U. Gross

The Skyrme nuclear energy density functional theory (DFT) is used to model neutron-induced fission in actinides. This paper focuses on the numerical implementation of the theory. In particular, it reports recent advances in DFT code…

Nuclear Theory · Physics 2015-06-12 N. Schunck

This contribution deals with the creation of numerical models for the simulation of the dynamic characteristics of fractional-order control systems and their comparison with analytical models. We give the results of the comparison of…

Optimization and Control · Mathematics 2007-05-23 L. Dorcak

Recent experimental advances in neuroscience have opened new vistas into the immense complexity of neuronal networks. This proliferation of data challenges us on two parallel fronts. First, how can we form adequate theoretical frameworks…

Neurons and Cognition · Quantitative Biology 2015-06-12 Madhu Advani , Subhaneil Lahiri , Surya Ganguli

Density functional theory (DFT) is one of the primary approaches to get a solution to the many-body Schrodinger equation. The essential part of the DFT theory is the exchange-correlation (XC) functional, which can not be obtained in…

Computational Physics · Physics 2021-12-10 Alexander Ryabov , Petr Zhilyaev

To make progress in science, we often build abstract representations of physical systems that meaningfully encode information about the systems. The representations learnt by most current machine learning techniques reflect statistical…

We present a theoretical framework based on an extension of dynamical density functional theory (DDFT) for describing the structure and dynamics of cells in living tissues and tumours. DDFT is a microscopic statistical mechanical theory for…

Soft Condensed Matter · Physics 2018-09-03 Hayder M. Al-Saedi , Andrew J. Archer , John Ward

Density functional theory (DFT) is an incredible success story. The low computational cost, combined with useful (but not yet chemical) accuracy, has made DFT a standard technique in most branches of chemistry and materials science.…

Chemical Physics · Physics 2015-06-03 Kieron Burke

Linear-response time-dependent Density Functional Theory (LR-TDDFT) is a widely used method for accurately predicting the excited-state properties of physical systems. Previous works have attempted to accelerate LR-TDDFT using heterogeneous…

Hardware Architecture · Computer Science 2025-04-07 Qingcai Jiang , Buxin Tu , Xiaoyu Hao , Junshi Chen , Hong An

Density functional theory (DFT) is shown to provide a novel conceptual and computational framework for entanglement in interacting many-body quantum systems. DFT can, in particular, shed light on the intriguing relationship between quantum…

Quantum Physics · Physics 2009-11-11 L. -A. Wu , M. S. Sarandy , D. A. Lidar , L. J. Sham

Statistical analysis of high-dimensional functional times series arises in various applications. Under this scenario, in addition to the intrinsic infinite-dimensionality of functional data, the number of functional variables can grow with…

Statistics Theory · Mathematics 2022-01-14 Qin Fang , Shaojun Guo , Xinghao Qiao

We review the progress that has been recently made in the application of time-dependent density functional theory to thermoelectric phenomena. As the field is very young, we emphasize open problems and fundamental issues. We begin by…

Mesoscale and Nanoscale Physics · Physics 2017-04-26 F. G. Eich , M. Di Ventra , G. Vignale

Density functional theory is the standard theory for computing the electronic structure of materials, which is based on a functional that maps the electron density to the energy. However, a rigorous form of the functional is not known and…

Materials Science · Physics 2021-12-02 Ryo Nagai , Ryosuke Akashi , Osamu Sugino

Recent advances at the intersection of control theory, neuroscience, and machine learning have revealed novel mechanisms by which dynamical systems perform computation. These advances encompass a wide range of conceptual, mathematical, and…

Machine Learning · Computer Science 2026-04-10 Arthur N. Montanari , Francesco Bullo , Dmitry Krotov , Adilson E. Motter

Recent developments in Machine Learning approaches for modelling physical systems have begun to mirror the past development of numerical methods in the computational sciences. In this survey, we begin by providing an example of this with…

Machine Learning · Computer Science 2023-04-04 Artur P. Toshev , Ludger Paehler , Andrea Panizza , Nikolaus A. Adams

A recent description of an exact map for the equilibrium structure and thermodynamics of a quantum system onto a corresponding classical system is summarized. Approximate implementations are constructed by pinning exact limits (ideal gas,…

Statistical Mechanics · Physics 2015-04-14 Jeffrey Wrighton , James Dufty , Sandipan Dutta

We introduce a spectral density functional theory which can be used to compute energetics and spectra of real strongly--correlated materials using methods, algorithms and computer programs of the electronic structure theory of solids. The…

Strongly Correlated Electrons · Physics 2009-11-10 S. Y. Savrasov , G. Kotliar

Density functional theory (DFT) exploits an independent-particle-system construction to replicate the densities and current of an interacting system. This construction is used here to access the exact effective potential and bias of…

Mesoscale and Nanoscale Physics · Physics 2018-03-30 Daniel Karlsson , Miroslav Hopjan , Claudio Verdozzi

Neuro-dynamic programming is a class of powerful techniques for approximating the solution to dynamic programming equations. In their most computationally attractive formulations, these techniques provide the approximate solution only…

Machine Learning · Computer Science 2016-04-18 Wei Chen , Dayu Huang , Ankur A. Kulkarni , Jayakrishnan Unnikrishnan , Quanyan Zhu , Prashant Mehta , Sean Meyn , Adam Wierman