Data-driven kernel designs for optimized greedy schemes: A machine learning perspective
T Wenzel, F Marchetti, E Perracchione - SIAM Journal on Scientific Computing, 2024 - SIAM
Thanks to their easy implementation via radial basis functions (RBFs), meshfree kernel
methods have proved to be an effective tool for, eg, scattered data interpolation, PDE …
methods have proved to be an effective tool for, eg, scattered data interpolation, PDE …
Be greedy and learn: efficient and certified algorithms for parametrized optimal control problems
We consider parametrized linear-quadratic optimal control problems and provide their
online-efficient solutions by combining greedy reduced basis methods and machine …
online-efficient solutions by combining greedy reduced basis methods and machine …
[HTML][HTML] Greedy kernel methods for approximating breakthrough curves for reactive flow from 3D porous geometry data
We address the challenging application of 3D pore scale reactive flow under varying
geometry parameters. The task is to predict time-dependent integral quantities, ie …
geometry parameters. The task is to predict time-dependent integral quantities, ie …
[PDF][PDF] Deep and greedy kernel methods: Algorithms, analysis and applications
T Wenzel - 2023 - elib.uni-stuttgart.de
Abstract Machine learning and in particular deep learning techniques are nowadays state of
the art and used in everyday life. Beyond, so-called kernel methods are another class of …
the art and used in everyday life. Beyond, so-called kernel methods are another class of …
Optimization Problems for PDEs in Weak Space-Time Form
Optimization problems constrained by time-dependent Partial Differential Equations (PDEs)
are challenging from a computational point of view: even in the simplest case, one needs to …
are challenging from a computational point of view: even in the simplest case, one needs to …