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SUMMARY:Machine Learning Methods for Solving Partial Differential Equation
 s
DTSTART:20230804T100000
DTEND:20230804T120000
DTSTAMP:20260407T051419Z
UID:15da30792dfe90bafc19eca7ec12d2a177eac0511754b750bb778607
CATEGORIES:Conferences - Seminars
DESCRIPTION:Anand George\nEDIC candidacy exam\nExam president: Prof. Rüdi
 ger Urbanke\nThesis advisor: Prof. Nicolas Macris\nCo-examiner: Prof. Oliv
 ier Lévêque\n\nAbstract\ncoming soon\n\nBackground papers\n 1) Stochas
 tic Controls - Hamiltonian Systems and HJB Equations\, In Chapter 7\, Se
 ctions 1\, 2 and 3.1 (pages 345-360)    https://link.springer.com/book/
 10.1007/978-1-4612-1466-3\n2) Algorithms for solving high dimensional PDEs
 : from nonlinear Monte Carlo to machine learning https://iopscience.iop.or
 g/article/10.1088/1361-6544/ac337f\n3) Random feature neural networks lea
 rn Black-Scholes type PDEs without curse of dimensionality https://arxiv.
 org/abs/2106.08900\n 
LOCATION:BC 133 https://plan.epfl.ch/?room==BC%20129
STATUS:CONFIRMED
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