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math.NA2024★ 3 cited
Variationally Correct Neural Residual Regression for Parametric PDEs: On the Viability of Controlled Accuracy
Markus Bachmayr, Wolfgang Dahmen, Mathias Oster
This paper is about learning the parameter-to-solution map for systems of partial differential equations (PDEs) that depend on a potentially large number of parameters covering all…
math.NA2024
A comparison study of supervised learning techniques for the approximation of high dimensional functions and feedback control
Mathias Oster, Luca Saluzzi, Tizian Wenzel
Approximation of high dimensional functions is in the focus of machine learning and data-based scientific computing. In many applications, empirical risk minimisation techniques ov…