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We study the second-order quasi-linear stochastic partial differential equations (SPDEs) defined on $C^1$ domains. The coefficients are random functions depending on $t,x$ and the unknown solutions. We prove the uniqueness and existence of…

Probability · Mathematics 2017-05-05 Ildoo Kim , Kyeong-hun Kim

We provide an algebraic framework to describe renormalization in regularity structures based on multi-indices for a large class of semi-linear stochastic PDEs. This framework is ``top-down", in the sense that we postulate the form of the…

Probability · Mathematics 2024-09-04 Yvain Bruned , Pablo Linares

We discuss a system of third order PDEs for strictly convex smooth functions on domains of Euclidean space. We argue that it may be understood as a closure of sorts of the first order prolongation of a family of second order PDEs. We…

Differential Geometry · Mathematics 2021-06-25 David Martínez Torres

Neural ordinary differential equations (ODEs) are an emerging class of deep learning models for dynamical systems. They are particularly useful for learning an ODE vector field from observed trajectories (i.e., inverse problems). We here…

Machine Learning · Computer Science 2023-05-23 Katharina Ott , Michael Tiemann , Philipp Hennig

We consider the variance renormalisation of a singular SPDE for which a Da Prato-Debussche trick is not applicable. The example taken is the $2$-dimensional generalised parabolic Anderson model (gPAM), driven by a much rougher than white…

Probability · Mathematics 2026-02-20 Máté Gerencsér , Yueh-Sheng Hsu

In this work, we translate at the level of decorated trees some of the crucial arguments which have been used in arXiv:2112.10739 for proposing a diagram-free approach for the convergence of the model in Regularity Structures. This allows…

Probability · Mathematics 2023-10-05 Yvain Bruned , Usama Nadeem

We study a general family of space-time discretizations of the KPZ equation and show that they converge to its solution. The approach we follow makes use of basic elements of the theory of regularity structures [M. Hairer, A theory of…

Probability · Mathematics 2018-01-11 Giuseppe Cannizzaro , Konstantin Matetski

One describes those double structures on rational normal curves which are defined scheme theoretically by quadratic equations and have linear syzygies, generalizing this way the double line in the plane

Algebraic Geometry · Mathematics 2007-05-23 Nicolae Manolache

In the article, we exhibit a series of new examples of rigid plane curves, that is, curves, whose collection of singularities determines them almost uniquely up to a projective transformation of the plane.

Algebraic Geometry · Mathematics 2015-06-29 Viktor S. Kulikov , Eugenii Shustin

We prove a general theorem on the stochastic convergence of appropriately renormalized models arising from nonlinear stochastic PDEs. The theory of regularity structures gives a fairly automated framework for studying these problems but…

Probability · Mathematics 2018-01-23 Ajay Chandra , Martin Hairer

Matrix-valued optimization tasks, including those involving symmetric positive definite (SPD) matrices, arise in a wide range of applications in machine learning, data science and statistics. Classically, such problems are solved via…

Optimization and Control · Mathematics 2024-10-15 Andrew Cheng , Melanie Weber

This paper is concerned with the regularity of solutions to linear and nonlinear evolution equations extending our findings in [22] to domains of polyhedral type. In particular, we study the smoothness in the specific scale…

Analysis of PDEs · Mathematics 2021-05-28 Stephan Dahlke , Cornelia Schneider

Smooth manifolds are not the suitable context for trying to generalize the concept of rough paths on a manifold. Indeed, when one is working with smooth maps instead of Lipschitz maps and trying to solve a rough differential equation, one…

Classical Analysis and ODEs · Mathematics 2019-11-13 Youness Boutaib , Terry Lyons

Training Neural Ordinary Differential Equations (ODEs) is often computationally expensive. Indeed, computing the forward pass of such models involves solving an ODE which can become arbitrarily complex during training. Recent works have…

Machine Learning · Computer Science 2020-11-03 Arnab Ghosh , Harkirat Singh Behl , Emilien Dupont , Philip H. S. Torr , Vinay Namboodiri

In this paper estimates for the uniform norm of solutions of parabolic SPDEs are derived. The result is obtained through iteration techniques, motivated by the work of Moser in deterministic settings. As an application of the main result,…

Probability · Mathematics 2016-09-09 Konstantinos Dareiotis , Máté Gerencsér

We develop a branched signature kernel solver for linear and nonlinear ordinary differential equations driven by a \emph{single observed trajectory} of a possibly rough forcing signal -- a setting that arises naturally in earthquake…

Numerical Analysis · Mathematics 2026-05-26 Munawar Ali , Qi Feng , Charlie Pyle , George Xu

We describe a smooth structure, called Fr\"olicher space, on CW complexes and spaces of triangulations. This structure enables differential methods for e.g. minimization of functionnals. As an application, we exhibit how an optimized…

Differential Geometry · Mathematics 2018-07-16 Jean-Pierre Magnot

This paper presents a comprehensive survey of methods which can be utilized to search for solutions to systems of nonlinear equations (SNEs). Our objectives with this survey are to synthesize pertinent literature in this field by presenting…

Mathematical Software · Computer Science 2022-08-19 Ilias S. Kotsireas , Panos M. Pardalos , Alexander Semenov , William T. Trevena , Michael N. Vrahatis

In this paper we study on smooth bounded domains the global regularity (up to the boundary) for weak solutions to systems having $p$-structure depending only on the symmetric part of the gradient.

Analysis of PDEs · Mathematics 2019-05-13 Luigi C. Berselli , Michael Ruzicka

Solutions to most nonlinear ordinary differential equations (ODEs) rely on numerical solvers, but this gives little insight into the nature of the trajectories and is relatively expensive to compute. In this paper, we derive analytic…

Dynamical Systems · Mathematics 2024-06-25 Estelle Basor , Rebecca Morrison
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