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It is well known that Malliavin calculus can be applied to a stochastic differential equation with Lipschitz continuous coefficients in order to clarify the existence and the smoothness of the solution. In this paper, we apply Malliavin…

Probability · Mathematics 2020-03-04 Shota Tsumurai

We close an unexpected gap in the literature of stochastic differential equations (SDEs) with drifts of super linear growth (and random coefficients), namely, we prove Malliavin and Parametric Differentiability of such SDEs. The former is…

Probability · Mathematics 2021-10-05 Peter Imkeller , Gonçalo dos Reis , William Salkeld

We prove an existence and uniqueness result for the obstacle problem for quasilinear stochastic integral-partial differential equations. Our method is based on the probabilistic interpretation of the solution using backward doubly SDEs with…

Probability · Mathematics 2018-06-08 Yuchao Dong , Xue Yang , Jing Zhang

The paper is concerned with the change of probability measures $\mu$ along non-random probability measure valued trajectories $\nu_t$, $t\in [-1,1]$. Typically solutions to non-linear PDEs, modeling spatial development as time progresses,…

Analysis of PDEs · Mathematics 2024-10-08 Jörg-Uwe Löbus

We analyze a class of nonlinear partial differential equations (PDEs) defined on $\mathbb{R}^d \times \mathcal{P}_2(\mathbb{R}^d),$ where $\mathcal{P}_2(\mathbb{R}^d)$ is the Wasserstein space of probability measures on $\mathbb{R}^d$ with…

Probability · Mathematics 2015-04-23 Jean-François Chassagneux , Dan Crisan , François Delarue

In this paper, we provide a direct approach to the existence and uniqueness of strong (in the probabilistic sense) and weak (in the PDE sense) solutions to quasilinear stochastic partial differential equations, which are neither monotone…

Analysis of PDEs · Mathematics 2015-01-06 Martina Hofmanova , Tusheng Zhang

In this work we mainly prove the existence and pathwise uniqueness of solutions to general backward doubly stochastic differential equations with jumps appearing in both forward and backward integral parts. Several comparison theorems under…

Probability · Mathematics 2017-04-12 Wei Xu

We consider the class of non-linear stochastic partial differential equations studied in \cite{conusdalang}. Equivalent formulations using integration with respect to a cylindrical Brownian motion and also the Skorohod integral are…

Probability · Mathematics 2015-03-25 Marta Sanz-Solé , André Süß

The classical Feynman-Kac identity builds a bridge between stochastic analysis and partial differential equations (PDEs) by providing stochastic representations for classical solutions of linear Kolmogorov PDEs. This opens the door for the…

Probability · Mathematics 2021-10-25 Christian Beck , Martin Hutzenthaler , Arnulf Jentzen

In this paper, we consider coupled forward-backward stochastic differential equations (FBSDEs in short) with parameter $\varepsilon >0$. We study the asymptotic behavior of its solutions and establish a large deviation principle for the…

Probability · Mathematics 2013-11-05 Ana Bela Cruzeiro , André de Oliveira Gomes , Liangquan Zhang

This work concerns about forward-backward multivalued stochastic systems. First of all, we prove one average principle for general stochastic differential equations in the $L^{2p}$ ($p\geq 1$) sense. Moreover, for $p=1$ a convergence rate…

Probability · Mathematics 2023-11-14 Huijie Qiao

We introduce a physics-driven deep latent variable model (PDDLVM) to learn simultaneously parameter-to-solution (forward) and solution-to-parameter (inverse) maps of parametric partial differential equations (PDEs). Our formulation…

Machine Learning · Statistics 2023-08-09 Arnaud Vadeboncoeur , Ömer Deniz Akyildiz , Ieva Kazlauskaite , Mark Girolami , Fehmi Cirak

We prove via a direct fixpoint argument the well-posedness of backward stochastic differential equations containing an additional drift driven by a path of finite $p$-variation with $p \in [1,2)$. An application to the Feynman-Kac…

Probability · Mathematics 2016-10-13 Joscha Diehl , Jianfeng Zhang

By using Malliavin calculus, explicit derivative formulae are established for a class of semi-linear functional stochastic partial differential equations with additive or multiplicative noise. As applications, gradient estimates and Harnack…

Probability · Mathematics 2011-10-25 Jianhai Bao , Feng-Yu Wang , Chenggui Yuan

We study the asymptotic behaviour of solutions of Forward Backward Stochastic Differential Equations in the coupled case, when the diffusion coefficient of the forward equation is multiplicatively perturbed by a small parameter that…

Probability · Mathematics 2013-02-27 Ana Bela Cruzeiro , André de Oliveira Gomes

The two-parameter Macdonald polynomials are a central object of algebraic combinatorics and representation theory. We give a Markov chain on partitions of k with eigenfunctions the coefficients of the Macdonald polynomials when expanded in…

Probability · Mathematics 2010-07-28 Persi Diaconis , Arun Ram

We study double-sided continued fractions whose coefficients are non-commuting symbols. We work within the formal approach of the Mal'cev-Neumann series and free division rings. We start with presenting the analogs of the standard results…

Exactly Solvable and Integrable Systems · Physics 2021-02-09 Adam Doliwa

In this paper backward stochastic differential equations with interaction (shorter BSDEs with interaction) are introduced. Far to our knowledge, this type of equation is not seen in the literature before. Existence and uniqueness result for…

Probability · Mathematics 2022-12-29 Jasmina Đorđević , Andrey Dorogovtsev

In this paper, a simplified second-order Gaussian Poincar\'e inequality for normal approximation of functionals over infinitely many Rademacher random variables is derived. It is based on a new bound for the Kolmogorov distance between a…

Probability · Mathematics 2023-01-31 Peter Eichelsbacher , Benedikt Rednoß , Christoph Thäle , Guangqu Zheng

We propose an approximation scheme for a class of semilinear parabolic equations that are convex and coercive in their gradients. Such equations arise often in pricing and portfolio management in incomplete markets and, more broadly, are…

Optimization and Control · Mathematics 2019-11-06 Shuo Huang , Gechun Liang , Thaleia Zariphopoulou