Numerical approximations of coupled forward-backward SPDEs with applications

Simple item page
dc.contributor.advisorQiu, Jinniao
dc.contributor.authorMolla, Md Hasib Uddin
dc.contributor.committeememberWare, Antony Frank
dc.contributor.committeememberSwishchuk, Anatoliy V.
dc.date2020-11
dc.date.accessioned2020-09-11T22:31:22Z
dc.date.available2020-09-11T22:31:22Z
dc.date.issued2020-09-10
dc.description.abstractWe introduce a new scheme combining the finite element method and machine learning techniques for the numerical approximations of coupled forward-backward stochastic partial differential equations (FBSPDEs) with homogeneous Dirichlet boundary conditions. For the FBSPDE, the finite element method in the spatial domain leads to approximations by finite-dimensional forward-backward stochastic differential equations (FBSDEs) in the temporal domain. We then approximate the solution of FBSDE by some existing machine learning schemes. Strong convergence results for spatial discretization of FBSPDEs are addressed.en_US
dc.identifier.citationMolla, Md. H. U. (2020). Numerical approximations of coupled forward-backward SPDEs with applications (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/38185
dc.identifier.urihttp://hdl.handle.net/1880/112515
dc.language.isoengen_US
dc.publisher.facultyScienceen_US
dc.publisher.institutionUniversity of Calgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.en_US
dc.subjectStochastic partial differential equationsen_US
dc.subjectdeep learningen_US
dc.subject.classificationEducation--Mathematicsen_US
dc.titleNumerical approximations of coupled forward-backward SPDEs with applicationsen_US
dc.typemaster thesisen_US
thesis.degree.disciplineMathematics & Statisticsen_US
thesis.degree.grantorUniversity of Calgaryen_US
thesis.degree.nameMaster of Science (MSc)en_US
ucalgary.item.requestcopytrueen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
ucalgary_2020_molla_md-hasib-uddin.pdf
Size:
595.71 KB
Format:
Adobe Portable Document Format
Description:
Main Article
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
2.62 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Open Theses and Dissertations