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In this article we consider the length functional defined on the space of immersed planar curves. The $L^2(ds)$ Riemannian metric gives rise to the curve shortening flow as the gradient flow of the length functional. Motivated by the…

Differential Geometry · Mathematics 2021-03-04 Philip Schrader , Glen Wheeler , Valentina-Mira Wheeler

Numerical simulations of quantum field theories on lattices serve as a fundamental tool for studying the non-perturbative regime of the theories, where analytic tools often fall short. Challenges arise when one takes the continuum limit or…

High Energy Physics - Lattice · Physics 2026-01-07 Miranda C. N. Cheng , Niki Stratikopoulou

The purpose of this manuscript is to review my recent activity on three main research topics. The first concerns the nature of low temperature amorphous solids and their relation with the spin glass transition in a magnetic field. This is…

Disordered Systems and Neural Networks · Physics 2024-05-13 Pierfrancesco Urbani

A recent line of work has shown remarkable behaviors of the generalization error curves in simple learning models. Even the least-squares regression has shown atypical features such as the model-wise double descent, and further works have…

Machine Learning · Statistics 2022-12-20 Antoine Bodin , Nicolas Macris

A slightly revised version of notes distributed during a short course on GPTs, given at the Perimeter Institute for Theoretical Physics in March and April of 2024.

Quantum Physics · Physics 2025-05-14 Alex Wilce

We consider a fractional generalization of Hamiltonian and gradient systems. We use differential forms and exterior derivatives of fractional orders. We derive fractional generalization of Helmholtz conditions for phase space. Examples of…

Dynamical Systems · Mathematics 2018-04-02 Vasily E. Tarasov

The scarcity of labeled data is a long-standing challenge for many machine learning tasks. We propose our gradient flow method to leverage the existing dataset (i.e., source) to generate new samples that are close to the dataset of interest…

Machine Learning · Computer Science 2023-11-06 Xinru Hua , Truyen Nguyen , Tam Le , Jose Blanchet , Viet Anh Nguyen

This article contains the lecture notes for the short course ``Introduction to Econophysics,'' delivered at the II Brazilian School on Statistical Mechanics, held in Sao Carlos, Brazil, in February 2004. The main goal of the present notes…

Other Condensed Matter · Physics 2017-08-23 Giovani L. Vasconcelos

Total variation gradient flows are important in several applied fields, including image analysis and materials science. In this paper, we review a few basic topics including definition of a solution, explicit examples and the notion of…

Analysis of PDEs · Mathematics 2024-01-31 Yoshikazu Giga , Hirotoshi Kuroda , Michał Łasica

This is the first part of the lecture notes that grew out of the special course given during the 2021-2022 academic year. In these lecture notes we present an approach to the fundamental structures of differential geometry that uses the…

Differential Geometry · Mathematics 2022-04-05 Dmitrii Pedchenko

These are expanded lecture notes for the summer school on Berkovich spaces that took place at the Institut de Math\'ematiques de Jussieu, Paris in 2010. They serve to illustrate some techniques and results from the dynamics on…

Dynamical Systems · Mathematics 2014-10-14 Mattias Jonsson

We develop a gradient-flow theory for time-dependent functionals defined in abstract metric spaces. Global well-posedness and asymptotic behavior of solutions are provided. Conditions on functionals and metric spaces allow to consider the…

Analysis of PDEs · Mathematics 2015-09-15 Lucas C. F. Ferreira , Julio C. Valencia-Guevara

This note illustrates the Ricci flow method based on the Cao.H.D's paper[1] and Yau.S.T's paper[4], and tries to explain the method in detail, especially in some calculations. Jian Song and Weinkove's note[9] used some other estimates to…

Analysis of PDEs · Mathematics 2022-11-22 Liu Chao

Sampling a target probability distribution with an unknown normalization constant is a fundamental challenge in computational science and engineering. Recent work shows that algorithms derived by considering gradient flows in the space of…

Machine Learning · Statistics 2024-03-12 Yifan Chen , Daniel Zhengyu Huang , Jiaoyang Huang , Sebastian Reich , Andrew M Stuart

This article is an invitation. It is, first, an invitation to consider as a subject worthy of attention the wide range of situations where small discrete elements, either bubbles, droplets or solid particles, are embedded in turbulent…

Fluid Dynamics · Physics 2023-11-06 Jean-Pierre Minier , Christophe Henry

We introduce a gradient flow formulation of linear Boltzmann equations. Under a diffusive scaling we derive a diffusion equation by using the machinery of gradient flows.

Mathematical Physics · Physics 2020-09-03 Giada Basile , Dario Benedetto , Lorenzo Bertini

These notes arose from a mini lecture series the author gave at the Early Career Researchers Workshop on Geometric Analysis and PDEs, held in January 2020 at the Matrix institute of the University of Melbourne. We discussed some classical…

Differential Geometry · Mathematics 2021-03-23 Julian Scheuer

We consider stochastic differential equations in a Hilbert space, perturbed by the gradient of a convex potential. We investigate the problem of convergence of a sequence of such processes. We propose applications of this method to…

Probability · Mathematics 2007-05-23 Lorenzo Zambotti

These are lecture notes that are based on the lectures from a class I taught on the topic of Randomized Linear Algebra (RLA) at UC Berkeley during the Fall 2013 semester.

Data Structures and Algorithms · Computer Science 2016-08-17 Michael W. Mahoney

Notes from lectures given at the Autumn School on Algebraic and Arithmetic Geometry at the Johannes Gutenberg-Universit\"at Mainz in October 2017.

Number Theory · Mathematics 2018-01-18 Martin Bright