no code implementations • 21 Dec 2023 • Ken Trotti, Samuel A. Cruz Alegría, Alena Kopaničáková, Rolf Krause
We propose to train neural networks (NNs) using a novel variant of the ``Additively Preconditioned Trust-region Strategy'' (APTS).
no code implementations • 31 Aug 2023 • Jan Verhülsdonk, Thomas Grandits, Francisco Sahli Costabal, Thomas Pinetz, Rolf Krause, Angelo Auricchio, Gundolf Haase, Simone Pezzuto, Alexander Effland
The efficient construction of anatomical models is one of the major challenges of patient-specific in-silico models of the human heart.
1 code implementation • 30 Jun 2023 • Alena Kopaničáková, Hardik Kothari, George Em Karniadakis, Rolf Krause
We propose to enhance the training of physics-informed neural networks (PINNs).
no code implementations • 15 Dec 2021 • Lia Gander, Simone Pezzuto, Ali Gharaviri, Rolf Krause, Paris Perdikaris, Francisco Sahli Costabal
Computational models of atrial fibrillation have successfully been used to predict optimal ablation sites.
no code implementations • 13 Sep 2021 • Claudio Tomasi, Rolf Krause
In this paper, we investigate the combination of multigrid methods and neural networks, starting from a Finite Element discretization of an elliptic PDE.
1 code implementation • 9 Aug 2021 • Cyrill von Planta, Alena Kopanicakova, Rolf Krause
We train deep residual networks with a stochastic variant of the nonlinear multigrid method MG/OPT.
no code implementations • 15 Jul 2021 • Alena Kopaničáková, Rolf Krause
We propose a globally convergent multilevel training method for deep residual networks (ResNets).
no code implementations • 22 Feb 2021 • Thomas Grandits, Simone Pezzuto, Francisco Sahli Costabal, Paris Perdikaris, Thomas Pock, Gernot Plank, Rolf Krause
In this work, we employ a recently developed approach, called physics informed neural networks, to learn the fiber orientations from electroanatomical maps, taking into account the physics of the electrical wave propagation.
no code implementations • 28 Jun 2020 • Vanessa Braglia, Alena Kopaničáková, Rolf Krause
Our multilevel training method constructs a multilevel hierarchy by reducing the number of samples.
no code implementations • 13 Apr 2020 • Lisa Gaedke-Merzhäuser, Alena Kopaničáková, Rolf Krause
For our examples we employ a multilevel gradient-based methods.
no code implementations • 31 Dec 2018 • Diego Ulisse Pizzagalli, Santiago Fernandez Gonzalez, Rolf Krause
Clustering is a technique for the analysis of datasets obtained by empirical studies in several disciplines with a major application for biomedical research.
no code implementations • 21 Aug 2018 • Luca Messina, Alessio Quaglino, Alexandra Goryaeva, Mihai-Cosmin Marinica, Christophe Domain, Nicolas Castin, Giovanni Bonny, Rolf Krause
Machine-learning techniques such as artificial neural networks are usually employed to work around this limitation and extract the needed parameters from large databases of high-fidelity data, but the latter are often computationally expensive to produce.