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We develop an \textit{a posteriori} error analysis for a numerical estimate of the time at which a functional of the solution to a partial differential equation (PDE) first achieves a threshold value on a given time interval. This quantity…

Numerical Analysis · Mathematics 2022-06-09 Jehanzeb Chaudhry , Don Estep , Trevor Giannini , Zachary Stevens , Simon Tavener

The paper is split in two parts: in the first part, we construct the exact likelihood for a discretely observed rough differential equation, driven by a piecewise linear path. In the second part, we use this likelihood in order to construct…

Statistics Theory · Mathematics 2018-07-10 Anastasia Papavasiliou , Kasia B. Taylor

Taylor expansions of analytic functions are considered with respect to two points. Cauchy-type formulas are given for coefficients and remainders in the expansions, and the regions of convergence are indicated. It is explained how these…

Classical Analysis and ODEs · Mathematics 2007-05-23 Jose L. Lopez , Nico M. Temme

We study a class of semi-implicit Taylor-type numerical methods that are easy to implement and designed to solve multidimensional stochastic differential equations driven by a general rough noise, e.g. a fractional Brownian motion. In the…

Numerical Analysis · Mathematics 2020-06-25 Sebastian Riedel , Yue Wu

Let $f(x)$ be a real function which has $(n+1)$-th derivative on an interval $[a, b]$. For any point $x_0\in (a, b)$ and any integer $0\leq k\leq n$, denote by $S_{k,x_0}(x)$ the $k$-th truncation of the Taylor expansion of $f(x)$ at $x_0$,…

Classical Analysis and ODEs · Mathematics 2020-05-12 Shun Tang

We show that infinitely differentiable solutions to parabolic and hyperbolic equations, whose right-hand sides are analytical in time, are also analytical in time at each fixed point of the space. These solutions are given in the form of…

Analysis of PDEs · Mathematics 2020-07-02 William G. Litvinov , Eugene Lytvynov

In numerous applications, surrogate models are used as a replacement for accurate parameter-to-observable mappings when solving large-scale inverse problems governed by partial differential equations (PDEs). The surrogate model may be a…

Optimization and Control · Mathematics 2025-12-08 Ruanui Nicholson , Radoslav Vuchkov , Umberto Villa , Noemi Petra

We investigate the scalar K pi form factor at low energies by the method of unitarity bounds adapted so as to include information on the phase and modulus along the elastic region of the unitarity cut. Using at input the values of the form…

High Energy Physics - Phenomenology · Physics 2014-11-20 Gauhar Abbas , B. Ananthanarayan , I. Caprini , I. Sentitemsu Imsong , S. Ramanan

In this paper, we derive an optimal first-order Taylor-like formula. In a seminal paper [14], we introduced a new first-order Taylor-like formula that yields a reduced remainder compared to the classical Taylor's formula. Here, we relax the…

Numerical Analysis · Mathematics 2023-11-27 Joël Chaskalovic , Franck Assous

An efficient approximate version of implicit Taylor methods for initial-value problems of systems of ordinary differential equations (ODEs) is introduced. The approach, based on an approximate formulation of Taylor methods, produces a…

Numerical Analysis · Mathematics 2024-02-05 Antonio Baeza , Raimund Bürger , María del Carmen Martí , Pep Mulet , David Zorío

We apply results of Malliavin-Thalmaier-Watanabe for strong and weak Taylor expansions of solutions of perturbed stochastic differential equations (SDEs). In particular, we work out weight expressions for the Taylor coefficients of the…

Computational Finance · Quantitative Finance 2008-12-10 Maria Siopacha , Josef Teichmann

The solution of a parabolic stochastic partial differential equation (SPDE) driven by an infinite-dimensional Brownian motion is in general not a semi-martingale anymore and does in general not satisfy an It\^{o} formula like the solution…

Probability · Mathematics 2010-10-04 Arnulf Jentzen , Peter Kloeden

Computing partial differential equation (PDE) operators via nested backpropagation is expensive, yet popular, and severely restricts their utility for scientific machine learning. Recent advances, like the forward Laplacian and randomizing…

Machine Learning · Computer Science 2025-11-25 Felix Dangel , Tim Siebert , Marius Zeinhofer , Andrea Walther

A two-parameter deformation of the Touchard polynomials, based on the NEXT $q$-exponential function of Tsallis, defines two statistics on set partitions. The generating function of classical Touchard polynomials is a composition of two…

Combinatorics · Mathematics 2022-08-10 Orli Herscovici

We prove mixed weak estimates of Sawyer type for fractional operators. More precisely, let $\mathcal{T}$ be either the maximal fractional function $M_\gamma$ or the fractional integral operator $I_\gamma$, $0<\gamma<n$, $1\leq p<n/\gamma$…

Analysis of PDEs · Mathematics 2017-12-25 Fabio Berra , Marilina Carena , Gladis Pradolini

We derive explicit tail-estimates for the Jacobian of the solution flow for stochastic differential equations driven by Gaussian rough paths. In particular, we deduce that the Jacobian has finite moments of all order for a wide class of…

Probability · Mathematics 2013-07-26 Thomas Cass , Christian Litterer , Terry Lyons

This short note provides an explicit description of the Fr\'echet derivatives of the principal square root matrix functional at any order. We present an original formulation that allows to compute sequentially the Fr\'echet derivatives of…

Numerical Analysis · Mathematics 2018-01-03 Pierre Del Moral , Angele Niclas

In this note we consider differential equations driven by a signal $x$ which is $\gamma$-H\"older with $\gamma>1/3$, and is assumed to possess a lift as a rough path. Our main point is to obtain existence of solutions when the coefficients…

Probability · Mathematics 2017-08-17 Prakash Chakraborty , Samy Tindel

In this paper we introduce a family of stochastic gradient estimation techniques based of the perturbative expansion around the mean of the sampling distribution. We characterize the bias and variance of the resulting Taylor-corrected…

Machine Learning · Statistics 2019-11-18 Luca Ambrogioni , Marcel A. J. van Gerven

In this brief, we discuss the implementation of a third order semi-implicit differentiator as a complement of the recent work by the author that proposes an interconnected semi-implicit Euler double differentiators algorithm through Taylor…

Numerical Analysis · Mathematics 2024-08-02 Loïc Michel , Jean-Pierre Barbot