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Found 12 papers, 2 papers with code
Parallel Trust-Region Approaches in Neural Network Training: Beyond Traditional Methods
We propose to train neural networks (NNs) using a novel variant of the ``Additively Preconditioned Trust-region Strategy'' (APTS).
Shape of my heart: Cardiac models through learned signed distance functions
The efficient construction of an anatomical model is one of the major challenges of patient-specific in-silico models of the human heart.
Enhancing training of physics-informed neural networks using domain-decomposition based preconditioning strategies
We propose to enhance the training of physics-informed neural networks (PINNs).
Fast characterization of inducible regions of atrial fibrillation models with multi-fidelity Gaussian process classification
Computational models of atrial fibrillation have successfully been used to predict optimal ablation sites.
Construction of Grid Operators for Multilevel Solvers: a Neural Network Approach
In this paper, we investigate the combination of multigrid methods and neural networks, starting from a Finite Element discretization of an elliptic PDE.
Training of deep residual networks with stochastic MG/OPT
We train deep residual networks with a stochastic variant of the nonlinear multigrid method MG/OPT.
Globally Convergent Multilevel Training of Deep Residual Networks
We propose a globally convergent multilevel training method for deep residual networks (ResNets).
Learning atrial fiber orientations and conductivity tensors from intracardiac maps using physics-informed neural networks
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.
A Multilevel Approach to Training
Our multilevel training method constructs a multilevel hierarchy by reducing the number of samples.
Multilevel Minimization for Deep Residual Networks
For our examples we employ a multilevel gradient-based methods.
A shortest-path based clustering algorithm for joint human-machine analysis of complex datasets
Clustering is a technique for the analysis of datasets obtained by empirical studies in several disciplines with a major application for biomedical research.
Smart energy models for atomistic simulations using a DFT-driven multifidelity approach
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.
