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Understanding natural symmetries is key to making sense of our complex and ever-changing world. Recent work has shown that neural networks can learn such symmetries directly from data using Hamiltonian Neural Networks (HNNs). But HNNs…

Machine Learning · Computer Science 2022-01-27 Andrew Sosanya , Sam Greydanus

It is shown that any singular Lagrangian theory: 1) can be formulated without the use of constraints by introducing a Clairaut-type version of the Hamiltonian formalism; 2) leads to a special kind of nonabelian gauge theory which is similar…

Mathematical Physics · Physics 2014-04-29 Steven Duplij

We prove the existence of a unitary transformation that enables two arbitrarily given Hamiltonians in the same Hilbert space to be transformed into one another. The result is straightforward yet, for example, it lays the foundation to…

Quantum Physics · Physics 2020-12-09 Lian-Ao Wu , Dvira Segal

Many important physical systems can be described as the evolution of a Hamiltonian system, which has the important property of being conservative, that is, energy is conserved throughout the evolution. Physics Informed Neural Networks and…

Machine Learning · Computer Science 2025-12-10 Harsh Choudhary , Chandan Gupta , Vyacheslav Kungurtsev , Melvin Leok , Georgios Korpas

In this note we present an example from undergraduate quantum mechanics designed to highlight the versatility of the Dirac $\delta$-function. Namely, we compute the expectation value of the Hamiltonian of a free-particle in a state…

General Physics · Physics 2020-07-28 Asim Gangopadhyaya , Constantin Rasinariu

In a vast class of systems, which includes members as diverse as sedimenting particles and bird flocks, interactions do not stem from a potential, and are in general nonreciprocal. Thus, it is not possible to define a conventional energy…

Statistical Mechanics · Physics 2026-04-03 Yu-Bo Shi , Roderich Moessner , Ricard Alert , Marin Bukov

The present article introduces a generalization of the (multisymplectic) Hamiltonian field theory for a Lagrangian density, allowing the formulation of this kind of field theories for variational problem of more general nature than those…

Mathematical Physics · Physics 2025-09-15 Guadalupe Quijón , Santiago Capriotti

We examine the fractional derivative of composite functions and present a generalization of the product and chain rules for the Caputo fractional derivative. These results are especially important for physical and biological systems that…

Classical Analysis and ODEs · Mathematics 2019-01-10 Gavriil Shchedrin , Nathanael C. Smith , Anastasia Gladkina , Lincoln D. Carr

We report on what seems to be an intriguing connection between variable integration time and partial velocity refreshment of Ideal Hamiltonian Monte Carlo samplers, both of which can be used for reducing the dissipative behavior of the…

Computation · Statistics 2023-09-20 Qijia Jiang

We study the theory of systems with constraints from the point of view of the formal theory of partial differential equations. For finite-dimensional systems we show that the Dirac algorithm completes the equations of motion to an…

High Energy Physics - Theory · Physics 2009-10-28 Werner M. Seiler , Robin W. Tucker

We present a general mechanism to establish the existence of diffusing orbits in a large class of nearly integrable Hamiltonian systems. Our approach relies on successive applications of the `outer dynamics' along homoclinic orbits to a…

Dynamical Systems · Mathematics 2017-04-26 Marian Gidea , Rafael de la Llave , Tere Seara

A new approach to dissipative quantum systems modelled by a system plus environment Hamiltonian is presented. Using a continuous sequence of infinitesimal unitary transformations the small quantum system is decoupled from its…

Statistical Mechanics · Physics 2009-10-30 Stefan Kehrein , Andreas Mielke

We extend Hamilton-Jacobi theory to Lagrange-Dirac (or implicit Lagrangian) systems, a generalized formulation of Lagrangian mechanics that can incorporate degenerate Lagrangians as well as holonomic and nonholonomic constraints. We refer…

Mathematical Physics · Physics 2012-09-13 Melvin Leok , Tomoki Ohsawa , Diana Sosa

While the definition of a fractional integral may be codified by Riemann and Liouville, an agreed-upon fractional derivative has eluded discovery for many years. This is likely a result of integral definitions including numerous constants…

Classical Analysis and ODEs · Mathematics 2018-10-10 Evan Camrud

This is an attempt to address diffusion phenomena from the point of view of information theory. We imagine a regular hamiltonian system under the random perturbation of thermal (molecular) noise and chaotic instability. The irregularity of…

Statistical Mechanics · Physics 2007-05-23 Qiuping A. Wang

We propose a linear finite-element discretization of Dirichlet problems for static Hamilton-Jacobi equations on unstructured triangulations. The discretization is based on simplified localized Dirichlet problems that are solved by a local…

Numerical Analysis · Mathematics 2025-10-20 Folkmar Bornemann , Christian Rasch

In high dimensions, reflective Hamiltonian Monte Carlo with inexact reflections exhibits slow mixing when the particle ensemble is initialised from a Dirac delta distribution and the uniform distribution is targeted. By quantifying the…

Machine Learning · Statistics 2026-03-20 Namu Kroupa , Gábor Csányi , Will Handley

Hamiltonian systems with functionally dependent constraints (irregular systems), for which the standard Dirac procedure is not directly applicable, are discussed. They are classified according to their behavior in the vicinity of the…

High Energy Physics - Theory · Physics 2009-11-10 Olivera Miskovic , Jorge Zanelli

An extension of Riewe's fractional Hamiltonian formulation is presented for fractional constrained systems. The conditions of consistency of the set of constraints with equations of motion are investigated. Three examples of fractional…

Mathematical Physics · Physics 2009-11-11 S. Muslih , D. Baleanu

Recently, continuous-time dynamical systems have proved useful in providing conceptual and quantitative insights into gradient-based optimization, widely used in modern machine learning and statistics. An important question that arises in…

Optimization and Control · Mathematics 2021-04-29 Guilherme França , Michael I. Jordan , René Vidal
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