Conference Proposal: "Nonlinear PDEs, Stochastic Control, and Filtering: New Methods and Applications"

Abstract

The objective of the meeting is to revisit classical problems and explore new directions where real world problems have been and can be successfully studied by an exchange of ideas and methods from PDE, probability, and numerical analysis. Topics of the meeting where both probabilistic and analytic methods have been essential to achieve major developments in recent years include the following. Nonlinear PDEs of parabolic and elliptic types arising in stochastic control problems (often called Bellman equations, or Hamilton-Jacobi-Bellman equations), and in stochastic differential games (Isaacs equation). These equations play important roles also in other areas of mathematics and applied fields. Monge Ampere equation, which is a special Bellman equation, arises, for example, in solving central problems in differential geometry and in optimal transport. Stochastic partial differential equations arising in nonlinear filtering (Zakai equations, Kushner-Shiryaev equations), in Biology (e.g., the Fleming-Viot and Dawson-Watanabe equations), in Engineering (e.g., stochastic Navier-Stokes equations) and in Physics (e.g., the Kardar-Parisi-Zhang equation).

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Document Details

Document Type
Technical Report
Publication Date
Sep 11, 2018
Accession Number
AD1084499

Entities

People

  • Hongjie Dong

Organizations

  • Brown University

Tags

DTIC Thesaurus Topics

  • Applied Mathematics
  • Computational Science
  • Differential Equations
  • Differential Geometry
  • Engineering
  • Equations
  • Filtration
  • Fluid Dynamics
  • Game Theory
  • Geometry
  • Information Operations
  • Mathematics
  • Numerical Analysis
  • Partial Differential Equations
  • Physics
  • Probability
  • Stochastic Control
  • Stochastic Processes
  • Theorems
  • Training

Fields of Study

  • Mathematics

Readers

  • Academic Conference Management
  • Finite Element Method (FEM) for solving Partial Differential Equations (PDEs)
  • Mathematical Modeling and Probability Theory.