16 citations · 22 across the 3 of their papers we have counts for
5 papers
Accelerating Training in Artificial Neural Networks with Dynamic Mode Decomposition
Mauricio E. Tano, Gavin D. Portwood, Jean C. Ragusa
Training of deep neural networks (DNNs) frequently involves optimizing several millions or even billions of parameters. Even with modern computing architectures, the computational…
Massively Parallel Transport Sweeps on Meshes with Cyclic Dependencies
Jan I C Vermaak, Jean C Ragusa, Jim E Morel
When solving the first-order form of the linear Boltzmann equation, a common misconception is that the matrix-free computational method of ``sweeping the mesh", used in conjunction…
Deep learning for 2D passive source detection in presence of complex cargo
Weston Baines, Peter Kuchment, Jean Ragusa
Methods for source detection in high noise environments are important for single-photon emission computed tomography (SPECT) medical imaging and especially crucial for homeland sec…
Accelerating PDE-constrained Inverse Solutions with Deep Learning and Reduced Order Models
Sheroze Sheriffdeen, Jean C. Ragusa, Jim E. Morel +2
Inverse problems are pervasive mathematical methods in inferring knowledge from observational and experimental data by leveraging simulations and models. Unlike direct inference me…
Acceleration of Radiation Transport Solves Using Artificial Neural Networks
Mauricio Tano, Jean Ragusa
Discontinuous Finite Element Methods (DFEM) have been widely used for solving radiation transport problems in participative and non-participative media. In the DFEM met…