# Brownian Motion and its Applications to Mathematical Analysis [electronic resource] :École d'Été de Probabilités de Saint-Flour XLIII – 2013 /

##### by Burdzy, Krzysztof [author.]; SpringerLink (Online service).

Material type: BookSeries: Lecture Notes in Mathematics: 2106Publisher: Cham : Springer International Publishing : 2014.Description: XII, 137 p. 16 illus., 4 illus. in color. online resource.ISBN: 9783319043944.Subject(s): Mathematics | Differential equations, partial | Potential theory (Mathematics) | Distribution (Probability theory) | Mathematics | Probability Theory and Stochastic Processes | Partial Differential Equations | Potential TheoryDDC classification: 519.2 Online resources: Click here to access onlineItem type | Current location | Call number | Status | Date due | Barcode |
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E-Books |
Central Library, IISER Bhopal
OPAC URL: http://webopac.iiserb.ac.in/ |
519.2 (Browse shelf) | Not for loan |

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1. Brownian motion -- 2. Probabilistic proofs of classical theorems -- 3. Overview of the "hot spots" problem -- 4. Neumann eigenfunctions and eigenvalues -- 5. Synchronous and mirror couplings -- 6. Parabolic boundary Harnack principle -- 7. Scaling coupling -- 8. Nodal lines -- 9. Neumann heat kernel monotonicity -- 10. Reflected Brownian motion in time dependent domains.

These lecture notes provide an introduction to the applications of Brownian motion to analysis and, more generally, connections between Brownian motion and analysis. Brownian motion is a well-suited model for a wide range of real random phenomena, from chaotic oscillations of microscopic objects, such as flower pollen in water, to stock market fluctuations. It is also a purely abstract mathematical tool which can be used to prove theorems in "deterministic" fields of mathematics. The notes include a brief review of Brownian motion and a section on probabilistic proofs of classical theorems in analysis. The bulk of the notes are devoted to recent (post-1990) applications of stochastic analysis to Neumann eigenfunctions, Neumann heat kernel and the heat equation in time-dependent domains.

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