Suchergebnis: Katalogdaten im Herbstsemester 2017

Physik Master Information
Wahlfächer
Physikalische und mathematische Wahlfächer
Auswahl: Mathematik
NummerTitelTypECTSUmfangDozierende
401-3531-00LDifferential Geometry I Information
Höchstens eines der drei Bachelor-Kernfächer
401-3461-00L Funktionalanalysis I / Functional Analysis I
401-3531-00L Differentialgeometrie I / Differential Geometry I
401-3601-00L Wahrscheinlichkeitstheorie / Probability Theory
ist im Master-Studiengang Mathematik anrechenbar.
W10 KP4V + 1UD. A. Salamon
KurzbeschreibungSubmanifolds of R^n, tangent bundle,
embeddings and immersions, vector fields, Lie bracket, Frobenius' Theorem.
Geodesics, exponential map, completeness, Hopf-Rinow.
Levi-Civita connection, parallel transport,
motions without twisting, sliding, and wobbling.
Isometries, Riemann curvature, Theorema Egregium.
Cartan-Ambrose-Hicks, symmetric spaces, constant curvature,
Hadamard's theorem.
LernzielIntroduction to Differential Geometry.
Submanifolds of Euclidean space, tangent bundle,
embeddings and immersions, vector fields and flows,
Lie bracket, foliations, the Theorem of Frobenius.
Geodesics, exponential map, injectivity radius, completeness
Hopf-Rinow Theorem, existence of minimal geodesics.
Levi-Civita connection, parallel transport, Frame bundle,
motions without twisting, sliding, and wobbling.
Isometries, the Riemann curvature tensor, Theorema Egregium.
Cartan-Ambrose-Hicks, symmetric spaces, constant curvature,
nonpositive sectional curvature, Hadamard's theorem.
LiteraturJoel Robbin and Dietmar Salamon "Introduction to Differential Geometry",
Link
401-3461-00LFunctional Analysis I Information
Höchstens eines der drei Bachelor-Kernfächer
401-3461-00L Funktionalanalysis I / Functional Analysis I
401-3531-00L Differentialgeometrie I / Differential Geometry I
401-3601-00L Wahrscheinlichkeitstheorie / Probability Theory
ist im Master-Studiengang Mathematik anrechenbar.
W10 KP4V + 1UA. Carlotto
KurzbeschreibungBaire category; Banach and Hilbert spaces, bounded linear operators; basic principles: Uniform boundedness, open mapping/closed graph theorem, Hahn-Banach; convexity; dual spaces; weak and weak* topologies; Banach-Alaoglu; reflexive spaces; compact operators and Fredholm theory; closed range theorem; spectral theory of self-adjoint operators in Hilbert spaces; Fourier transform and applications.
LernzielAcquire a good degree of fluency with the fundamental concepts and tools belonging to the realm of linear Functional Analysis, with special emphasis on the geometric structure of Banach and Hilbert spaces, and on the basic properties of linear maps.
SkriptLecture Notes on "Funktionalanalysis I" by Michael Struwe
LiteraturA primary reference for the course is the textbook by H. Brezis:

Haim Brezis. Functional analysis, Sobolev spaces and partial differential equations. Universitext. Springer, New York, 2011.

Other useful, and recommended references are the following:

Elias M. Stein and Rami Shakarchi. Functional analysis (volume 4 of Princeton Lectures in Analysis). Princeton University Press, Princeton, NJ, 2011.

Peter D. Lax. Functional analysis. Pure and Applied Mathematics (New York). Wiley-Interscience [John Wiley & Sons], New York, 2002.

Walter Rudin. Functional analysis. International Series in Pure and Applied Mathematics. McGraw-Hill, Inc., New York, second edition, 1991.
Voraussetzungen / BesonderesSolid background on the content of all Mathematics courses of the first two years of the undergraduate curriculum at ETH (most remarkably: fluency with measure theory, Lebesgue integration and L^p spaces).
401-3601-00LProbability Theory
Höchstens eines der drei Bachelor-Kernfächer
401-3461-00L Funktionalanalysis I / Functional Analysis I
401-3531-00L Differentialgeometrie I / Differential Geometry I
401-3601-00L Wahrscheinlichkeitstheorie / Probability Theory
ist im Master-Studiengang Mathematik anrechenbar.
W10 KP4V + 1UA.‑S. Sznitman
KurzbeschreibungBasics of probability theory and the theory of stochastic processes in discrete time
LernzielThis course presents the basics of probability theory and the theory of stochastic processes in discrete time. The following topics are planned:
Basics in measure theory, random series, law of large numbers, weak convergence, characteristic functions, central limit theorem, conditional expectation, martingales, convergence theorems for martingales, Galton Watson chain, transition probability, Theorem of Ionescu Tulcea, Markov chains.
InhaltThis course presents the basics of probability theory and the theory of stochastic processes in discrete time. The following topics are planned:
Basics in measure theory, random series, law of large numbers, weak convergence, characteristic functions, central limit theorem, conditional expectation, martingales, convergence theorems for martingales, Galton Watson chain, transition probability, Theorem of Ionescu Tulcea, Markov chains.
Skriptavailable, will be sold in the course
LiteraturR. Durrett, Probability: Theory and examples, Duxbury Press 1996
H. Bauer, Probability Theory, de Gruyter 1996
J. Jacod and P. Protter, Probability essentials, Springer 2004
A. Klenke, Wahrscheinlichkeitstheorie, Springer 2006
D. Williams, Probability with martingales, Cambridge University Press 1991
401-3621-00LFundamentals of Mathematical Statistics Information W10 KP4V + 1US. van de Geer
KurzbeschreibungThe course covers the basics of inferential statistics.
Lernziel
401-3177-67LIntroduction to Vertex Operator Algebras Information W4 KP2VC. A. Keller
KurzbeschreibungA first introduction to the theory of vertex operator algebras.
LernzielUnderstand the basic concepts of vertex operator algebras and their most important examples.
InhaltTentative plan:

1) Formal power series, local fields
2) Vertex Algebras
3) Conformal symmetry
4) Vertex Operator Algebras
5) Correlation functions
6) VOAs from lattices
7) Connection to modular forms: Zhu's Theorem
8) Connection to Monstrous Moonshine
LiteraturVictor Kac: Vertex Algebras for Beginners

James Lepowksy, Haisheng Li: Introduction to Vertex Operator Algebras and Their Representations
Voraussetzungen / BesonderesBasic algebra and linear algebra. Some background in quantum mechanics is helpful, but not necessary.
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