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We introduce a stochastic version of the optimal transport problem. We provide an analysis by means of the study of the associated Hamilton-Jacobi-Bellman equation, which is set on the set of probability measures. We introduce a new…

Analysis of PDEs · Mathematics 2024-05-22 Charles Bertucci

Classical field theory is considered as a theory of unparametrized surfaces embedded in a configuration space, which accommodates, in a symmetric way, spacetime positions and field values. Dynamics is defined by a (Hamiltonian) constraint…

Mathematical Physics · Physics 2016-06-20 Vaclav Zatloukal

We study a variant of the dynamical optimal transport problem in which the energy to be minimised is modulated by the covariance matrix of the distribution. Such transport metrics arise naturally in mean-field limits of certain ensemble…

Analysis of PDEs · Mathematics 2024-12-23 Martin Burger , Matthias Erbar , Franca Hoffmann , Daniel Matthes , André Schlichting

The methods of supergravity allow us to derive a multi-field F-term potential. Using this, we denote a generic and non-positive single-field F-term potential. We insert our theory into the scalar-gravity part of the $SU(2,1|1)$ invariant…

High Energy Physics - Theory · Physics 2021-04-12 Ugur Atli , Omer Guleryuz

Optimal transport is a geometrically intuitive, robust and flexible metric for sample comparison in data analysis and machine learning. Its formal Riemannian structure allows for a local linearization via a tangent space approximation. This…

Optimization and Control · Mathematics 2024-06-07 Clément Sarrazin , Bernhard Schmitzer

We consider the problem of time-optimal path planning for simple nonholonomic vehicles. In previous similar work, the vehicle has been simplified to a point mass and the obstacles have been stationary. Our formulation accounts for a…

Optimization and Control · Mathematics 2021-11-22 Christian Parkinson , Madeline Ceccia

We consider the optimal mass transportation problem in $\RR^d$ with measurably parameterized marginals, for general cost functions and under conditions ensuring the existence of a unique optimal transport map. We prove a joint measurability…

Probability · Mathematics 2008-09-09 Joaquin Fontbona , Helene Guerin , Sylvie Meleard

Wasserstein distances are metrics on probability distributions inspired by the problem of optimal mass transportation. Roughly speaking, they measure the minimal effort required to reconfigure the probability mass of one distribution in…

Methodology · Statistics 2019-04-10 Victor M. Panaretos , Yoav Zemel

Quadratic Wasserstein distances are obtained between dynamical systems (with states as special case), on $\mathbb{Z}_2$-graded von Neumann algebras. This is achieved through a systematic translation from non-graded to $\mathbb{Z}_2$-graded…

Mathematical Physics · Physics 2025-10-06 Rocco Duvenhage , Dylan van Zyl , Paola Zurlo

Large optimal transport problems can be approached via domain decomposition, i.e. by iteratively solving small partial problems independently and in parallel. Convergence to the global minimizers under suitable assumptions has been shown in…

Optimization and Control · Mathematics 2021-06-16 Mauro Bonafini , Ismael Medina , Bernhard Schmitzer

An important unsolved problem that affects practically all attempts to connect string theory to cosmology and phenomenology is how to distinguish effective field theories belonging to the string landscape from those that are not consistent…

High Energy Physics - Theory · Physics 2019-02-20 Ana Achúcarro , Gonzalo A. Palma

Two geometrical structures have been extensively studied for a manifold of probability distributions. One is based on the Fisher information metric, which is invariant under reversible transformations of random variables, while the other is…

Optimization and Control · Mathematics 2017-10-02 Shun-ichi Amari , Ryo Karakida , Masafumi Oizumi

The Wasserstein distance, rooted in optimal transport (OT) theory, is a popular discrepancy measure between probability distributions with various applications to statistics and machine learning. Despite their rich structure and…

Machine Learning · Statistics 2023-03-02 Sloan Nietert , Rachel Cummings , Ziv Goldfeld

The presentation covers prerequisite results from Topology and Measure Theory. This is then followed by an introduction into couplings and basic definitions for optimal transport. The Kantrorovich problem is then introduced and an existence…

Probability · Mathematics 2020-10-12 Austin Vandegriffe

The Wasserstein distance received a lot of attention recently in the community of machine learning, especially for its principled way of comparing distributions. It has found numerous applications in several hard problems, such as domain…

Machine Learning · Statistics 2017-10-23 Nicolas Courty , Rémi Flamary , Mélanie Ducoffe

This paper is devoted to variational problems on the set of probability measures which involve optimal transport between unequal dimensional spaces. In particular, we study the minimization of a functional consisting of the sum of a term…

Analysis of PDEs · Mathematics 2019-11-18 Luca Nenna , Brendan Pass

On the path towards quantum gravity, we find friction between temporal relations in quantum mechanics (QM) (where they are fixed and field-independent), and in general relativity (where they are field-dependent and dynamic). This paper aims…

General Relativity and Quantum Cosmology · Physics 2017-11-28 Henrique Gomes

The Hartle-Hawking no-boundary proposal describes the quantum creation of the universe. To have a non-negligible probability to obtain a classical expanding universe, eternal inflation is required, which is severely constrained by Swampland…

General Relativity and Quantum Cosmology · Physics 2020-11-03 Hiroki Matsui , Takahiro Terada

We investigate a first-order mean field planning problem of the form \begin{equation} \left\lbrace\begin{aligned} -\partial_t u + H(x,Du) &= f(x,m) &&\text{in } (0,T)\times \mathbb{R}^d, \\ \partial_t m - \nabla\cdot (m\,H_p(x,Du)) &= 0…

Analysis of PDEs · Mathematics 2019-08-05 Carlo Orrieri , Alessio Porretta , Giuseppe Savaré

Flow-based methods for sampling and generative modeling use continuous-time dynamical systems to represent a {transport map} that pushes forward a source measure to a target measure. The introduction of a time axis provides considerable…

Machine Learning · Statistics 2025-06-19 Panos Tsimpos , Zhi Ren , Jakob Zech , Youssef Marzouk
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