numerical methods

Efficient Numerical Methods for High-Dimensional Approximation Problems

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B4 L5 R5220

I​n the field of uncertainty quantification, the effects of parameter uncertainties on scientific simulations may be studied by integrating or approximating a quantity of interest as a function over the parameter space. If this is done numerically, using regular grids with a fixed resolution, the required computational work increases exponentially with respect to the number of uncertain parameters -- a phenomenon known as the curse of dimensionality.

Lauri Mustonen

Lauri Mustonen is a Former visiting student from Aalto University at Professor Raul F. Tempone's Stochastic Numerics Research Group (STOCHNUM) at King Abdullah University of Science and Technology (KAUST). Research Interests Lauri's research interests include uncertainty Quantification, Inverse Problems, Numerical Methods. Education Profile M.Sc. (Tech.), Engineering Physics, and Mathematics, Aalto University, 2014. Professional Memberships http://math.aalto.fi/en/research/applied/inverseproblems/

André Gustavo Carlon

Postdoctoral Research Fellow, Stochastic Numerics Research Group
André Gustavo Carlon is a Postdoctoral Fellow at Stochastic Numerics Research Group under the supervision of Professor Raul F. Tempone at King Abdullah University of Science and Technology (KAUST). Research Interests André's research interests include the analysis of stochastic optimization methods, ​the application of stochastic gradient methods to engineering problems, and Bayesian optimal experimental design with non-linear models. Selected Publications​ A. G. Carlon, L. Espath, R. Tempone. Approximating Hessian matrices using Bayesian inference: a new approach for quasi-Newton methods in

Aku Jaakko Alexis Kammonen

Aku Jaakko Alexis Kammonen is a Postdoctoral Fellow at Stochastic Numerics Research Group under the supervision of Professor Raul F. Tempone at King Abdullah University of Science and Technology (KAUST). Research Interests Aku's research interests include Machine Learning, Neural Networks, Random Features, Spectral Bias, Adversarial Attacks, and Numerical Analysis. Selected Publications A. Kammonen, L. Liang, A. Pandey, and R. Tempone (2024, March 19–23). Comparing Spectral Bias and Robustness For Two-Layer Neural Networks: SGD vs Adaptive Random Fourier Features [Poster presentation]