17 citations · 33 across the 17 of their papers we have counts for
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Adaptive non-intrusive reconstruction of solutions to high-dimensional parametric PDEs
Martin Eigel, Nando Farchmin, Sebastian Heidenreich +1
Numerical methods for random parametric PDEs can greatly benefit from adaptive refinement schemes, in particular when functional approximations are computed as in stochastic Galerk…
Dynamical low-rank approximations of solutions to the Hamilton-Jacobi-Bellman equation
Martin Eigel, Reinhold Schneider, David Sommer
We present a novel method to approximate optimal feedback laws for nonlinear optimal control based on low-rank tensor train (TT) decompositions. The approach is based on the Dirac-…
Efficient approximation of high-dimensional exponentials by tensornetworks
Martin Eigel, Nando Farchmin, Sebastian Heidenreich +1
In this work a general approach to compute a compressed representation of the exponential of a high-dimensional function is presented. Such exponential functions play…
Pricing high-dimensional Bermudan options with hierarchical tensor formats
Christian Bayer, Martin Eigel, Leon Sallandt +1
An efficient compression technique based on hierarchical tensors for popular option pricing methods is presented. It is shown that the "curse of dimensionality" can be alleviated f…