| The Athena++ Adaptive Mesh Refinement Framework: Design and Magnetohydrodynamic Solvers JM Stone, K Tomida, CJ White, KG Felker Astrophysical Journal Supplement Series 249 (1), 2020 | 157 | 2020 |
| A fourth-order accurate finite volume method for ideal MHD via upwind constrained transport KG Felker, JM Stone Journal of Computational Physics 375, 1365-1400, 2018 | 59 | 2018 |
| Multi-core performance studies of a Monte Carlo neutron transport code AR Siegel, K Smith, PK Romano, B Forget, KG Felker The International Journal of High Performance Computing Applications 28 (1 …, 2014 | 37 | 2014 |
| The effect of load imbalances on the performance of Monte Carlo algorithms in LWR analysis AR Siegel, K Smith, PK Romano, B Forget, K Felker Journal of Computational Physics 235, 901-911, 2013 | 23 | 2013 |
| Improved cache performance in Monte Carlo transport calculations using energy banding A Siegel, K Smith, K Felker, P Romano, B Forget, P Beckman Computer Physics Communications 185 (4), 1195-1199, 2014 | 13 | 2014 |
| Fully convolutional spatio-temporal models for representation learning in plasma science G Dong, KG Felker, A Svyatkovskiy, W Tang, J Kates-Harbeck Journal of Machine Learning for Modeling and Computing 2 (1), 2021 | 12 | 2021 |
| Athena++: Radiation GR magnetohydrodynamics code JM Stone, K Tomida, C White, KG Felker Astrophysics Source Code Library, ascl: 1912.005, 2019 | 10 | 2019 |
| The energy band memory server algorithm for parallel Monte Carlo transport calculations KG Felker, AR Siegel, KS Smith, PK Romano, B Forget SNA+ MC 2013-Joint International Conference on Supercomputing in Nuclear …, 2014 | 8 | 2014 |
| Optimizing memory constrained environments in Monte Carlo nuclear reactor simulations KG Felker, AR Siegel, SF Siegel The International Journal of High Performance Computing Applications 27 (2 …, 2013 | 7 | 2013 |
| Exploration of quantum machine learning and ai accelerators for fusion science M Liu, G Dong, KG Felker, M Otten, P Balaprakash, W Tang, Y Alexeev Argonne National Lab.(ANL), Argonne, IL (United States), 2022 | 1 | 2022 |
| Implementation of AI/DEEP learning disruption predictor into a plasma control system W Tang, G Dong, J Barr, K Erickson, R Conlin, D Boyer, J Kates‐Harbeck, ... Contributions to Plasma Physics 63 (5-6), e202200095, 2023 | | 2023 |
| Tokamak Disruption Predictions Based on Deep Learning Temporal Convolutional Neural Networks G Dong, K Felker, A Svyatkovskiy, W Tang, J Kates-Harbeck APS Division of Plasma Physics Meeting Abstracts 2020, BO05. 002, 2020 | | 2020 |
| High-order Finite Volume Methods for Magnetohydrodynamics with Applications in Computational Astrophysics KG Felker Princeton University, 2019 | | 2019 |
Andrew R. SiegelSenior Scientist, Argonne National LaboratoryVerified email at mcs.anl.gov
