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Scientific Computing and Machine Learning
SCML
Scientific Computing and Machine Learning
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Event Content - (D)

First Letter
A (10) | B (1) | C (4) | D (5) | E (3) | F (2) | H (2) | I (1) | M (3) | N (5) | O (3) | P (1) | S (5) | T (1) | U (1) | V (1)
KAUST-CEMSE-AMCS-MAC-Tao-Zhou-Deep-Adpative-Sampling
Nov 19, 16:00 - 17:00, Deep Adaptive Sampling for Numerical PDEs (B1, L3, R3119), Prof. Tao Zhou, Chinese Academy of Sciences
Mathematics and Applications Colloquium
Dec 20, 19:00 - 20:00, Deep Neural Network Approximation via Function Compositions (KAUST), Dr. Shijun Zhang, Department of Mathematics, Duke University
approximation, deep neural networks, Seminar
KAUST-CEMSE-AMCS-Mathematics-and-Applications-Colloquium-Cinzia-Soresina
Jan 13, 14:30 - 15:30, Derivation of cross-diffusion models in population dynamics: dichotomy, time-scales, and fast-reaction (B1, L3, R3119), Cinzia Soresina, Montalcini Tenure-Track Assistant Professor, Department of Mathematics, University of Trento, Italy
Mathematics and Applications Colloquium
Sep 26, 16:00 - 17:00, Device accelerated solvers for PDEs. Current status and future perspectives (B5 L5 R5220), Stefano Zampini, Senior Research Scientist, Hierarchical Computations on Manycore Architectures
GPU, PDEs, petsc, Public Colloquium, Shaheen, supercomputing
Mar 25, 16:00 - 17:00, Dimension Liftings for Quantum Computation of Partial Differential Equations and Related Problems (B1 L3 R3119), Shi Jin, Director of Institute of Natural Sciences, Chair Professor of Mathematics, Shanghai Jiao Tong University
Distinguished Lectures of Mathematics and Applications, PDEs, quantum computing, quantum simulation, Schrödinger equation

Scientific Computing and Machine Learning (SCML)

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