Christoph Schwab: Katalogdaten im Frühjahrssemester 2020

NameHerr Prof. Dr. Christoph Schwab
LehrgebietMathematik
Adresse
Seminar für Angewandte Mathematik
ETH Zürich, HG G 57.1
Rämistrasse 101
8092 Zürich
SWITZERLAND
Telefon+41 44 632 35 95
Fax+41 44 632 10 85
E-Mailchristoph.schwab@sam.math.ethz.ch
URLhttp://www.sam.math.ethz.ch/~schwab
DepartementMathematik
BeziehungOrdentlicher Professor

NummerTitelECTSUmfangDozierende
401-1652-10LNumerische Mathematik I Information Belegung eingeschränkt - Details anzeigen 6 KP3V + 2UC. Schwab
KurzbeschreibungDieser Kurs gibt eine Einführung in numerische Methoden für Studierende der Mathematik im 2. Semester. Abgedeckt werden Methoden der linearen Algebra (lineare Gleichungssysteme, Matrixeigenwertprobleme) sowie der Analysis (Nullstellensuche von Funktionen sowie numerische Interpolation, Integration und Approximation) in Theorie und Implementierung.
LernzielKenntnis der grundlegenden numerischen Verfahren sowie `numerische Kompetenz':
Anwendung der numerischen Verfahren zur Problemloesung,
Mathematische Beweistechniken fuer den Nachweis von Stabilitaet, Konsistenz u. Konvergenz der Verfahren sowie deren MATLAB Implementierung.
InhaltRundungsfehler, lineare Gleichungssysteme, nichtlineare Gleichungen (Skalar und Systeme), Interpolation, Extrapolation, lineare und nichtlineare Ausgleichsrechnung, elementare Optimierungsverfahren, numerische Integration.
SkriptSkript zur Vorlesung sowie Leseliste sind auf der Webseite der Vorlesung verfügbar.
LiteraturSkript wird eingeschriebenen Studierenden des ETH BSc Mathematik zur
Verfuegung gestellt.
_Zusaetzlich_ wird empfohlen:
Quarteroni, Sacco und Saleri, Numerische Mathematik 1 + 2, Springer Verlag 2002.
Voraussetzungen / BesonderesZulassungsbedingungen:
Linear Algebra I , Analysis I in ETH BSc MATH
u. parallele Belegung von
Linear Algebra II, Analysis II in ETH BSc MATH

Woechentliche Hausuebungsserien sind integraler
Bestandteil des Kurses; die Hausuebungen
involvieren MATLAB Programmieraufgaben, u.
werden bewertet.
401-3650-19LNumerical Analysis Seminar: Mathematics of Deep Neural Network Approximation Belegung eingeschränkt - Details anzeigen
Number of participants limited to 6. Priority will be given to MSc students who did not complete a seminar.
4 KP2SC. Schwab
KurzbeschreibungThis seminar will review recent _mathematical results_ on approximation power of deep neural networks (DNNs). The focus will be on mathematical proof techniques to obtain approximation rate estimates (in terms of neural network size and connectivity) on various classes of input data including, in particular, selected types of PDE solutions.
Lernziel
InhaltPresentation of the Seminar:
Deep Neural Networks (DNNs) have recently attracted substantial
interest and attention due to outperforming the best established
techniques in a number of tasks (Chess, Go, Shogi,
autonomous driving, language translation, image classification, etc.).
In many cases, these successes have been achieved by
heuristic implementations combined
with massive compute power and training data.
For a (bird's eye) overview, see
https://arxiv.org/abs/1901.05639
and, more mathematical and closer to the seminar theme,
https://arxiv.org/abs/1901.02220

This seminar will review recent _mathematical results_
on approximation power of deep neural networks (DNNs).
The focus will be on mathematical proof techniques to
obtain approximation rate estimates (in terms of neural network
size and connectivity) on various classes of input data
including, in particular, selected types of PDE solutions.
Mathematical results support that DNNs can
equalize or outperform the best mathematical results
known to date.

Particular cases comprise:
high-dimensional parametric maps,
analytic and holomorphic maps,
maps containing multi-scale features which arise as solution classes from PDEs,
classes of maps which are invariant under group actions.
Voraussetzungen / BesonderesEach seminar topic will allow expansion to a semester or a
master thesis in the MSc MATH or MSc Applied MATH.

The seminar format will be oral student presentations in
the first half of May 2020, combined with a written report.
Student presentations will be
based on a recent research paper selected in two meetings
at the start of the semester (end of February).

Disclaimer:
The seminar will _not_ address recent developments in DNN software,
such as training heuristics, or programming techniques
for DNN training in various specific applications.
401-4658-00LComputational Methods for Quantitative Finance: PDE Methods Information Belegung eingeschränkt - Details anzeigen 6 KP3V + 1UC. Schwab
KurzbeschreibungIntroduction to principal methods of option pricing. Emphasis on PDE-based methods. Prerequisite MATLAB programming
and knowledge of numerical mathematics at ETH BSc level.
LernzielIntroduce the main methods for efficient numerical valuation of derivative contracts in a
Black Scholes as well as in incomplete markets due Levy processes or due to stochastic volatility
models. Develop implementation of pricing methods in MATLAB.
Finite-Difference/ Finite Element based methods for the solution of the pricing integrodifferential equation.
Inhalt1. Review of option pricing. Wiener and Levy price process models. Deterministic, local and stochastic
volatility models.
2. Finite Difference Methods for option pricing. Relation to bi- and multinomial trees.
European contracts.
3. Finite Difference methods for Asian, American and Barrier type contracts.
4. Finite element methods for European and American style contracts.
5. Pricing under local and stochastic volatility in Black-Scholes Markets.
6. Finite Element Methods for option pricing under Levy processes. Treatment of
integrodifferential operators.
7. Stochastic volatility models for Levy processes.
8. Techniques for multidimensional problems. Baskets in a Black-Scholes setting and
stochastic volatility models in Black Scholes and Levy markets.
9. Introduction to sparse grid option pricing techniques.
SkriptThere will be english, typed lecture notes as well as MATLAB software for registered participants in the course.
LiteraturR. Cont and P. Tankov : Financial Modelling with Jump Processes, Chapman and Hall Publ. 2004.

Y. Achdou and O. Pironneau : Computational Methods for Option Pricing, SIAM Frontiers in Applied Mathematics, SIAM Publishers, Philadelphia 2005.

D. Lamberton and B. Lapeyre : Introduction to stochastic calculus Applied to Finance (second edition), Chapman & Hall/CRC Financial Mathematics Series, Taylor & Francis Publ. Boca Raton, London, New York 2008.

J.-P. Fouque, G. Papanicolaou and K.-R. Sircar : Derivatives in financial markets with stochastic volatility, Cambridge Univeristy Press, Cambridge, 2000.

N. Hilber, O. Reichmann, Ch. Schwab and Ch. Winter: Computational Methods for Quantitative Finance, Springer Finance, Springer, 2013.
401-5650-00LZurich Colloquium in Applied and Computational Mathematics Information 0 KP1KR. Abgrall, R. Alaifari, H. Ammari, R. Hiptmair, S. Mishra, S. Sauter, C. Schwab
KurzbeschreibungForschungskolloquium
Lernziel