57 citations · 82 across the 9 of their papers we have counts for
10 papers
scikit-rom: An Open-Source Python Platform for Teaching and Prototyping Projection-Based Reduced-Order Modeling
Suparno Bhattacharyya, Ali Syed, Jian Tao +1
Projection-based reduced-order modeling (ROM) has become a cornerstone technique for accelerating parameter-intensive engineering simulations, yet the methodology remains challengi…
Residual-Driven Lifting Identification for Nonlinear-Manifold Reduced-Order Models of Parametrized Linear PDEs
Francesco A. B. Silva, Jean C. Ragusa, Theron Guo +1
We introduce a residual-driven procedure for training nonlinear-manifold reduced-order models for parametrized linear partial differential equations that, given prescribed latent a…
Parametric Dynamic Mode Decomposition for Reduced Order Modeling
Quincy A. Huhn, Mauricio E. Tano, Jean C. Ragusa +1
Dynamic Mode Decomposition (DMD) is a model-order reduction approach, whereby spatial modes of fixed temporal frequencies are extracted from numerical or experimental data sets. Th…
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…
Positive asymptotic preserving approximation of the radiation transport equation
Jean-Luc Guermond, Bojan Popov, Jean Ragusa
We introduce a (linear) positive and asymptotic preserving method or solving the one-group radiation transport equation. The approximation in space is discretization agnostic: the…
Detection of small low emission sources - case studies
A. Olson, A. Ciabatti, Y. Hristova +4
The article is devoted to a further study of the Compton camera method of passive detection of small amounts of special nuclear materials, developed by the authors in their previou…