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Related papers: New non-local SUSY KdV conservation laws from a re…

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Numerical schemes that conserve invariants have demonstrated superior performance in various contexts, and several unified methods have been developed for constructing such schemes. However, the mathematical properties of these schemes…

Numerical Analysis · Mathematics 2024-12-23 Shuto Kawai , Shun Sato , Takayasu Matsuo

Stochastic gradient descent (SGD) has been a go-to algorithm for nonconvex stochastic optimization problems arising in machine learning. Its theory however often requires a strong framework to guarantee convergence properties. We hereby…

Optimization and Control · Mathematics 2025-03-11 Azar Louzi

In this paper I review the multiplet calculus of $N = 1$, $D = 1$ local supersymmetry with applications to the construction of models for spinning particles in background fields, and models with space-time supersymmetry. New features…

High Energy Physics - Theory · Physics 2016-11-03 J. W. van Holten

Drifting models have recently gained attention for generating high-quality samples in a single forward pass. During training, they learn a push-forward map by following a vector-valued field, the drift field. We ask whether this procedure…

Machine Learning · Computer Science 2026-05-11 Leonard T. Franz , Sebastian Hoffmann , Tim Weiland , Bernhard Schölkopf , Georg Martius

This paper deals with a singular nonlocal phase field system of conserved type.Colli--K.\ [Nonlinear Anal.\ 190 (2020)] have derived existence of solutions to a singular phase field system of conserved type. On the other hand,…

Analysis of PDEs · Mathematics 2022-08-29 Shunsuke Kurima

Discovering conservation laws for a given dynamical system is important but challenging. In a theorist setup (differential equations and basis functions are both known), we propose the Sparse Invariant Detector (SID), an algorithm that…

Dynamical Systems · Mathematics 2023-07-06 Ziming Liu , Patrick Obin Sturm , Saketh Bharadwaj , Sam Silva , Max Tegmark

We propose new numerical approach to non-conservative dynamical systems. Our method being of low order, enhances qualitative performance of standard discrete gradient algorithm, thank to new concept of a reservoir. Paper is of explanatory…

Numerical Analysis · Mathematics 2020-02-18 Artur Kobus

We show that the supersymmetric nonlinear Schr\"odinger equation can be written as a constrained super KP flow in a nonstandard representation of the Lax equation. We construct the conserved charges and show that this system reduces to the…

High Energy Physics - Theory · Physics 2015-06-26 J. C. Brunelli , A. Das

Utilizing some conservation laws of (1+1)-dimensional integrable local evolution systems, it is conjectured that higher dimensional integrable equations may be regularly constructed by a deformation algorithm. The algorithm can be applied…

Exactly Solvable and Integrable Systems · Physics 2023-03-22 S. Y. Lou , Xia-zhi Hao , Man Jia

We provide a systematic and practical method of deriving 5D supergravity action described by 4D superfields on a general warped geometry, including a non-BPS background. Our method is based on the superconformal formulation of 5D…

High Energy Physics - Theory · Physics 2009-11-10 Hiroyuki Abe , Yutaka Sakamura

Non-autonomous Svinolupov-Jordan systems are considered. The integrability criteria of such systems are associated with the existence of recursion operators. A new non-autonomous KdV system is obtained and its recursion operator is given…

Exactly Solvable and Integrable Systems · Physics 2009-11-07 Metin Gurses , Atalay Karasu , Refik Turhan

A few new N=2 superintegrable mappings in the (1|2) superspace are proposed and their origin is analyzed. Using one of them, acting like the discrete symmetry transformation of the N=2 supersymmetric modified NLS hierarchy, the recursion…

High Energy Physics - Theory · Physics 2009-10-30 A. Sorin

We analyze stochastic gradient algorithms for optimizing nonconvex problems. In particular, our goal is to find local minima (second-order stationary points) instead of just finding first-order stationary points which may be some bad…

Machine Learning · Computer Science 2019-06-24 Zhize Li

We introduce a refinement of the persistence diagram, the graded persistence diagram. It is the Mobius inversion of the graded rank function, which is obtained from the rank function using the unary numeral system. Both persistence diagrams…

Algebraic Topology · Mathematics 2022-05-09 Leo Betthauser , Peter Bubenik , Parker B. Edwards

In this paper, we present a novel investigation of the so-called SAV approach, which is a framework to construct linearly implicit geometric numerical integrators for partial differential equations with variational structure. SAV approach…

Numerical Analysis · Mathematics 2021-05-11 Tomoya Kemmochi , Shun Sato

Doubly special relativity (DSR) is usually regarded as a low-energy limit of a quantum gravity theory with testable predictions. On the other hand, non-local quantum field theories have been presented as a solution to the inconsistencies…

High Energy Physics - Theory · Physics 2025-02-24 J. J. Relancio , L. Santamaría-Sanz

We prove that the Ising models with transverse and longitudinal fields on the hypercubic lattices with dimensions higher than one have no local conserved quantities other than the Hamiltonian. This holds for any value of the longitudinal…

Statistical Mechanics · Physics 2025-05-20 Yuuya Chiba

In a recent paper Dargis and Mathieu introduced integrodifferential odd flows for the supersymmetric KdV equation. These flows are obtained from the nonlocal conservation laws associated with the fourth root of its Lax operator. In this…

High Energy Physics - Theory · Physics 2015-06-26 E. Ramos

For every mapping of a perturbed spacetime onto a background and with any vector field $\xi$ we construct a conserved covariant vector density $I(\xi)$, which is the divergence of a covariant antisymmetric tensor density, a…

General Relativity and Quantum Cosmology · Physics 2009-11-11 Joseph Katz , Jiri Bicak , Donald Lynden-Bell

Stochastic Gradient Descent (SGD) is an out-of-equilibrium algorithm used extensively to train artificial neural networks. However very little is known on to what extent SGD is crucial for to the success of this technology and, in…

Machine Learning · Computer Science 2023-12-19 Persia Jana Kamali , Pierfrancesco Urbani