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Towards multi-fidelity machine learning in scientific computing on GPU clusters
Host: Prof. Michael Multerer
USI Lugano Campus, room A-34, Red building
University of Basel, Switzerland
The solution of parametric partial differential equations or other parametric problems is the main component of many applications in scientific computing. Such applications include, but are not limited to, uncertainty quantification, inverse problems and optimization. To avoid the re-implementation of scientific simulation codes, the use of snapshot-based (non-intrusive) techniques for the solution of parametric problems becomes very attractive.
In this presentation, I will report on ongoing work to solve parametric problems with a higher-dimensional parameter space by means of approximation in reproducing kernel Hilbert spaces. In presence of regularization, approximation in reproducing kernel Hilbert spaces is equivalent to the so-called "kernel ridge regression", which is a classical approach in machine learning. In that sense, results on the use of machine learning to for an efficient approximation of parametric problems will be discussed for examples in computational fluid mechanics and quantum chemistry.
Peter Zaspel got his PhD in mathematics at the University of Bonn. After a Postdoc at the University of Heidelberg / HITS, he now works as Postdoc at the University of Basel. His research interests are in higher-dimensional approximation (with uncertainty quantification and machine learning), algebraic linear solvers, high performance computing (e.g. GPUs) and applications.
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