monte carlo algorithms

Overcoming the curse of dimensionality: from nonlinear Monte Carlo to deep learning

Arnulf Jentzen, Professor, Applied Mathematics Münster: Institute for Analysis and Numerics, University of Münster

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KAUST

In this talk we prove that suitable deep neural network approximations do indeed overcome the curse of dimensionality in the case of a general class of semilinear parabolic PDEs and we thereby prove, for the first time, that a general semilinear parabolic PDE can be solved approximatively without the curse of dimensionality.

Meet KAUST prospective student: Miguel Ángel Álvarez Ballesteros

1 min read ·
Miguel Ángel Álvarez Ballesteros is a physics graduate from the Universidad Industrial de Santander, Bucaramanga, Colombia. Miguel will join KAUST this fall as a M.S./Ph.D. candidate and member of the KAUST Computational Probability research group under the supervision of Professor Ajay Jasra.

Hamza M. Ruzayqat

Research Scientist, Omar Knio Research Group
Dr. Hamza Ruzayqat is a Research Scientist at KAUST, specializing in Monte Carlo algorithms, data assimilation, and uncertainty quantification, with a background that includes a PhD in Mathematics from the University of Tennessee-Knoxville and various teaching and research positions.