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Grant: Discovery Grants Program - Individual

University of Alberta

$90,000.00

Professor

EF 2022-23

Résumé
This proposal focuses on stochastic differential systems driven by fractional Brownian motions, stochastic heat equations driven by general Gaussian noises and Dirichlet processes. In particular, it is proposed the following projects. (1) Construct parameter estimators for nonlinear stochastic differential systems driven by fractional Brownian motions of Hurst parameter H1/2 to the case H1/2. It is planned to study the existence and uniqueness of this equation for Hurst parameter H<1/2. Global solution will also be studied. (4) Find the broadest condition on covariance structure of the noise so that the stochastic heat equation has a unique classical solution. Of course the best one is the necessary and sufficient condition and it is intended to search such condition. (5) For fixed t and x, u(t,x) is a random variable, which is of continuous type, namely, it has a density. It is planned to have a better understanding of this density, for example, to obtain the asymptotic behavior of this density. (6) Establish an Ito formula for stochastic heat equation so that the formula can be applied to the Cole-Hopf transformation and then study rigorously the relation between stochastic heat equation and KPZ equation. (7) Extend the Brox diffusion from one dimension to high dimension. Namely, study the high dimensional Brownian motion in fractional noisy environment and study the branching processes where each individual follows a Brox diffusion. (7) Study other stochastic partial differential equations such as stochastic elliptic equations with boundary conditions and fractional order stochastic partial differential equations. (8) Explore if the Gaussian analysis will be useful in machine learning or not. (9) Dirichlet processes play important role in Bayesian nonparametrics and have application in machine learning. It is proposed to bring more stochastic analysis including methodology from nonlinear filtering to the study of Dirichlet processes. (10) Study random matrix theory and seek if there is any relation with stochastic heat equation.
Programme
Discovery Grants Program - Individual
Bénéficiaire
Emplacement
Alberta, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data

Tel que publié

Application id
RGPIN-2018-05687
Name nom
Hu, Yaozhong
Department département
Mathematical and Statistical Sciences - Mathematical and Statistical Sciences
Organization id
9
Institution établissement
University of Alberta
Province en
Alberta
Province fr
Alberta
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2022
Competition year année de concours
2018
Award amount
90000
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
DISCOVERY RESEARCH
Group fr
RECHERCHE AXÉE SUR LA DÉCOUVERTE
Committee code
1508
Committee name en
Mathematics and Statistics
Committee name fr
Mathématiques et statistique
Area of application code
1205
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Mathematical sciences
Area of application fr
Sciences mathématiques
Research subject code
3000
Research subject group en
Statistics and probability
Research subject group fr
Statistique et probabilité
Research subject en
Statistics and probability
Research subject fr
Statistique et probabilité
Installment
inst:5;6
Partie
2
Nb partie
6
Application title
Professor
Application summary
This proposal focuses on stochastic differential systems driven by fractional Brownian motions, stochastic heat equations driven by general Gaussian noises and Dirichlet processes. In particular, it is proposed the following projects. (1) Construct parameter estimators for nonlinear stochastic differential systems driven by fractional Brownian motions of Hurst parameter H1/2 to the case H1/2. It is planned to study the existence and uniqueness of this equation for Hurst parameter H<1/2. Global solution will also be studied. (4) Find the broadest condition on covariance structure of the noise so that the stochastic heat equation has a unique classical solution. Of course the best one is the necessary and sufficient condition and it is intended to search such condition. (5) For fixed t and x, u(t,x) is a random variable, which is of continuous type, namely, it has a density. It is planned to have a better understanding of this density, for example, to obtain the asymptotic behavior of this density. (6) Establish an Ito formula for stochastic heat equation so that the formula can be applied to the Cole-Hopf transformation and then study rigorously the relation between stochastic heat equation and KPZ equation. (7) Extend the Brox diffusion from one dimension to high dimension. Namely, study the high dimensional Brownian motion in fractional noisy environment and study the branching processes where each individual follows a Brox diffusion. (7) Study other stochastic partial differential equations such as stochastic elliptic equations with boundary conditions and fractional order stochastic partial differential equations. (8) Explore if the Gaussian analysis will be useful in machine learning or not. (9) Dirichlet processes play important role in Bayesian nonparametrics and have application in machine learning. It is proposed to bring more stochastic analysis including methodology from nonlinear filtering to the study of Dirichlet processes. (10) Study random matrix theory and seek if there is any relation with stochastic heat equation.