From the 1 of 11 linked papers with an AI index.
3 citations · 3 across the 4 of their papers we have counts for
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Solving stochastic partial differential equations using neural networks in the Wiener chaos expansion
Ariel Neufeld, Philipp Schmocker
In this paper, we solve stochastic partial differential equations (SPDEs) numerically by using (possibly random) neural networks in the truncated Wiener chaos expansion of their co…
Global universal approximation of functional input maps on weighted spaces
Christa Cuchiero, Philipp Schmocker, Josef Teichmann
We introduce so-called functional input neural networks defined on a possibly infinite dimensional weighted space with values also in a possibly infinite dimensional output space.…
Universal approximation results for neural networks with non-polynomial activation function over non-compact domains
Ariel Neufeld, Philipp Schmocker
This paper extends the universal approximation property of single-hidden-layer feedforward neural networks beyond compact domains, which is of particular interest for the approxima…