Results 281 to 290 of about 925,909 (345)

Asymptotic stability of fractional order (1,2] stochastic delay differential equations in Banach spaces

Chaos, Solitons and Fractals, 2021
Ajeet Singh, A. Shukla, V. Vijayakumar, R. Udhayakumar   +3 more
semanticscholar   +3 more sources

Stochastic Differential Equations

The Control Systems Handbook, 2018
semanticscholar   +3 more sources

Stochastic Differential Equations

1985
B. Øksendal
openaire   +2 more sources

Stochastic differential equations

2011
In this chapter we present some basic results on stochastic differential equations, hereafter shortened to SDEs, and we examine the connection to the theory of parabolic partial differential equations.
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MULTIVALUED STOCHASTIC PARTIAL DIFFERENTIAL EQUATIONS VIA BACKWARD DOUBLY STOCHASTIC DIFFERENTIAL EQUATIONS

Stochastics and Dynamics, 2008
In this paper, we establish by means of Yosida approximation, the existence and uniqueness of the solution of a backward doubly stochastic differential equation whose coefficient contains the subdifferential of a convex function. We will use this result to prove the existence of stochastic viscosity solution for some multivalued parabolic stochastic ...
Boufoussi, B., Mrhardy, N.
openaire   +2 more sources

Optimal controls for second‐order stochastic differential equations driven by mixed‐fractional Brownian motion with impulses

Mathematical methods in the applied sciences, 2020
We study optimal control problems for a class of second‐order stochastic differential equation driven by mixed‐fractional Brownian motion with non‐instantaneous impulses. By using stochastic analysis theory, strongly continuous cosine family, and a fixed
Rajesh Dhayal, Muslim Malik, Syed Abbas, A. Debbouche   +3 more
semanticscholar   +1 more source

Deep Learning-Based Numerical Methods for High-Dimensional Parabolic Partial Differential Equations and Backward Stochastic Differential Equations

Communications in Mathematics and Statistics, 2017
We study a new algorithm for solving parabolic partial differential equations (PDEs) and backward stochastic differential equations (BSDEs) in high dimension, which is based on an analogy between the BSDE and reinforcement learning with the gradient of ...
W. E, Jiequn Han, Arnulf Jentzen
semanticscholar   +1 more source

Stochastic Differential Equations

2016
Let \(\mathbf{W} = (W^{1},\ldots,W^{m})\) be an m-dimensional Brownian motion, and let $$\displaystyle{\boldsymbol{\sigma }= (\sigma _{ij})_{1\leq i\leq d,1\leq j\leq m}: [0,\infty ) \times \mathbb{R}^{d} \rightarrow \mathbb{R}^{d} \times \mathbb{R}^{m}}$$ and $$\displaystyle{\boldsymbol{\mu }= (\mu ^{1},\ldots,\mu ^{d}): [0,\infty ) \times \
Paola Lecca, Angela Re, Adaoha Ihekwaba, Ivan Mura, Thanh-Phuong Nguyen   +4 more
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Stochastic partial differential equations

2014
Second order stochastic partial differential equations are discussed from a rough path point of view. In the linear and finite-dimensional noise case we follow a Feynman–Kac approach which makes good use of concentration of measure results, as those obtained in Sect. 11.2.
Peter K. Friz, Martin Hairer
openaire   +1 more source

Semantic Image Inversion and Editing using Rectified Stochastic Differential Equations

International Conference on Learning Representations
Generative models transform random noise into images; their inversion aims to transform images back to structured noise for recovery and editing.
Litu Rout, Yujia Chen, Nataniel Ruiz, C. Caramanis, Sanjay Shakkottai, Wen-Sheng Chu   +5 more
semanticscholar   +1 more source