| Genetic-algorithm-optimized neural networks for gravitational wave classification DS Deighan, SE Field, CD Capano, G Khanna Neural Computing and Applications 33 (20), 13859-13883, 2021 | 23 | 2021 |
| Mining spatio-temporal data on industrialization from historical registries D Berenbaum, D Deighan, T Marlow, A Lee, S Frickel, M Howison arXiv preprint arXiv:1612.00992, 2016 | 21 | 2016 |
| ChemTab: A Physics Guided Chemistry Modeling Framework A Salunkhe, D Deighan, PE DesJardin, V Chandola International Conference on Computational Science, 75-88, 2022 | 2 | 2022 |
| Physics informed machine learning for chemistry tabulation A Salunkhe, D Deighan, PE DesJardin, V Chandola Journal of Computational Science 69, 102001, 2023 | 1 | 2023 |
| Simulation Workflows in Minutes, at Scale for Next-Generation HPC A Grosvenor, A Zemlyansky, D Deighan, D Sysko Smoky Mountains Computational Sciences and Engineering Conference, 288-300, 2022 | 1 | 2022 |
| Optimized convolutional neural networks for the detection of multimodal gravitational wave signals S Field, C Capano, D Deighan, G Khanna APS April Meeting Abstracts 2019, X10. 004, 2019 | | 2019 |
| Exploring Convolutional Neural Networks for Gravitational Wave data analysis DS Deighan Bulletin of the American Physical Society 63, 2018 | | 2018 |
Mark HowisonSr. Director of Research and Technology, Research Improving People's LivesVerified email at ripl.org
