4 citations · 8 across the 5 of their papers we have counts for
7 papers
Toward Parallel in Time for Chaotic Dynamical Systems
David A. Vargas, Robert D. Falgout, Stefanie Günther +1
As CPU clock speeds have stagnated, and high performance computers continue to have ever higher core counts, increased parallelism is needed to take advantage of these new architec…
Quandary: An open-source C++ package for high-performance optimal control of open quantum systems
Stefanie Günther, N. Anders Petersson, Jonathan L. Dubois
Quantum optimal control can be used to shape the control pulses for realizing unitary and non-unitary transformations of quantum states. These control pulses provide the fundamenta…
Spline parameterization of neural network controls for deep learning
Stefanie Günther, Will Pazner, Dongping Qi
Based on the continuous interpretation of deep learning cast as an optimal control problem, this paper investigates the benefits of employing B-spline basis functions to parameteri…
Multilevel Initialization for Layer-Parallel Deep Neural Network Training
Eric C. Cyr, Stefanie Günther, Jacob B. Schroder
This paper investigates multilevel initialization strategies for training very deep neural networks with a layer-parallel multigrid solver. The scheme is based on the continuous in…
Layer-Parallel Training of Deep Residual Neural Networks
S. Günther, L. Ruthotto, J. B. Schroder +2
Residual neural networks (ResNets) are a promising class of deep neural networks that have shown excellent performance for a number of learning tasks, e.g., image classification an…
A Non-Intrusive Parallel-in-Time Approach for Simultaneous Optimization with Unsteady PDEs
Stefanie Günther, Nicolas R. Gauger, Jacob B. Schroder
This paper presents a non-intrusive framework for integrating existing unsteady partial differential equation (PDE) solvers into a parallel-in-time simultaneous optimization algori…