Random walks for solving Robin boundary value problems and sampling in a bounded domain

A weak-sense numerical method to approximate reflected stochastic differential equations (RSDEs) is proposed and analysed. The method is simple to implement. It is proved that the method has the first order of weak convergence. Together with the Monte Carlo technique, it can be used to numerically s...

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Main Author: Sharma, Akash
Format: Thesis (University of Nottingham only)
Language:English
Published: 2022
Subjects:
Online Access:https://eprints.nottingham.ac.uk/69123/
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author Sharma, Akash
author_facet Sharma, Akash
author_sort Sharma, Akash
building Nottingham Research Data Repository
collection Online Access
description A weak-sense numerical method to approximate reflected stochastic differential equations (RSDEs) is proposed and analysed. The method is simple to implement. It is proved that the method has the first order of weak convergence. Together with the Monte Carlo technique, it can be used to numerically solve linear parabolic and elliptic PDEs with Robin boundary condition. One of the key results of this paper is the use of the proposed method for computing ergodic limits, i.e. expectations with respect to the invariant law of RSDEs, both inside a domain in Rd and on its boundary. This allows to efficiently sample from distributions with compact support. Both time-averaging and ensemble-averaging estimators are considered and analysed. A new second-order weak approximation method is also presented and investigated. The case of arbitrary oblique direction of reflection is also considered. Further, a new adaptive weak scheme to solve a Poisson PDE with Neumann boundary condition is proposed and is analysed with regards to convergence and cost. The presented theoretical results are supported by several numerical experiments.
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format Thesis (University of Nottingham only)
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institution University of Nottingham Malaysia Campus
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publishDate 2022
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spelling nottingham-691232025-02-28T15:15:18Z https://eprints.nottingham.ac.uk/69123/ Random walks for solving Robin boundary value problems and sampling in a bounded domain Sharma, Akash A weak-sense numerical method to approximate reflected stochastic differential equations (RSDEs) is proposed and analysed. The method is simple to implement. It is proved that the method has the first order of weak convergence. Together with the Monte Carlo technique, it can be used to numerically solve linear parabolic and elliptic PDEs with Robin boundary condition. One of the key results of this paper is the use of the proposed method for computing ergodic limits, i.e. expectations with respect to the invariant law of RSDEs, both inside a domain in Rd and on its boundary. This allows to efficiently sample from distributions with compact support. Both time-averaging and ensemble-averaging estimators are considered and analysed. A new second-order weak approximation method is also presented and investigated. The case of arbitrary oblique direction of reflection is also considered. Further, a new adaptive weak scheme to solve a Poisson PDE with Neumann boundary condition is proposed and is analysed with regards to convergence and cost. The presented theoretical results are supported by several numerical experiments. 2022-08-02 Thesis (University of Nottingham only) NonPeerReviewed application/pdf en cc_by https://eprints.nottingham.ac.uk/69123/1/4342118_Akash_PhD_thesis_with_corrections.pdf Sharma, Akash (2022) Random walks for solving Robin boundary value problems and sampling in a bounded domain. PhD thesis, University of Nottingham. stochastic differential equations partial differential equation Robin boundary value problems
spellingShingle stochastic differential equations
partial differential equation
Robin boundary value problems
Sharma, Akash
Random walks for solving Robin boundary value problems and sampling in a bounded domain
title Random walks for solving Robin boundary value problems and sampling in a bounded domain
title_full Random walks for solving Robin boundary value problems and sampling in a bounded domain
title_fullStr Random walks for solving Robin boundary value problems and sampling in a bounded domain
title_full_unstemmed Random walks for solving Robin boundary value problems and sampling in a bounded domain
title_short Random walks for solving Robin boundary value problems and sampling in a bounded domain
title_sort random walks for solving robin boundary value problems and sampling in a bounded domain
topic stochastic differential equations
partial differential equation
Robin boundary value problems
url https://eprints.nottingham.ac.uk/69123/