| Physics-informed neural networks for gravity field modeling of the Earth and Moon J Martin, H Schaub Celestial Mechanics and Dynamical Astronomy 134 (2), 13, 2022 | 28 | 2022 |
| Robust chauvenet outlier rejection MP Maples, DE Reichart, NC Konz, TA Berger, AS Trotter, JR Martin, ... The Astrophysical Journal Supplement Series 238 (1), 2, 2018 | 26 | 2018 |
| The fading of Cassiopeia A, and improved models for the absolute spectrum of primary radio calibration sources AS Trotter, DE Reichart, RE Egger, J Stýblová, ML Paggen, JR Martin, ... Monthly Notices of the Royal Astronomical Society 469 (2), 1299-1313, 2017 | 23 | 2017 |
| Physics-informed neural networks for gravity field modeling of small bodies J Martin, H Schaub Celestial Mechanics and Dynamical Astronomy 134 (5), 46, 2022 | 22 | 2022 |
| The Physics-Informed Neural Network Gravity Model Revisited: Model Generation III JR Martin, H Schaub AAS/AIAA Space Flight Mechanics Conference, 2023 | 6 | 2023 |
| GPGPU Implementation of Pines’ Spherical Harmonic Gravity Model JR Martin, H Schaub Univelt Inc., Escondido, 2020 | 5 | 2020 |
| Preliminary Analysis of Small-Body Gravity Field Estimation using Physics-Informed Neural Networks and Kalman Filters JR Martin, H Schaubb International Astronautical Congress, 2022 | 4 | 2022 |
| Reinforcement learning and orbit-discovery enhanced by small-body physics-informed neural network gravity models J Martin, H Schaub AIAA SCITECH 2022 Forum, 2272, 2022 | 4 | 2022 |
| Skynet algorithm for single-dish radio mapping. I. Contaminant-Cleaning, mapping, and photometering small-scale structures JR Martin, DE Reichart, DA Dutton, MP Maples, TA Berger, FD Ghigo, ... The Astrophysical Journal Supplement Series 240 (1), 12, 2019 | 3 | 2019 |
| Applications of Physics-Informed Neural Networks for Gravity Field Modeling JR Martin, H Schaub AAS/AIAA Space Flight Mechanics, 2021 | 2 | 2021 |
| The Physics-Informed Neural Network Gravity Model: Generation III J Martin, H Schaub arXiv preprint arXiv:2312.10257, 2023 | 1 | 2023 |
| Periodic Orbit Discovery Enhanced by Physics-Informed Neural Networks JR Martin, H Schaub 2022 Astrodynamics Specialist Conference, Charlotte, North Carolina, 7-11, 2022 | 1 | 2022 |
| Physics-Informed Neural Networks for Gravity Field Modeling JR Martin University of Colorado at Boulder, 2023 | | 2023 |
| Using Artificial Neural Networks for Offline Gravimetry JR Martin, H Schaub AAS/AIAA Astrodynamics Specialist Conference, 2020 | | 2020 |
| INVESTIGATING THE FUSION OF MASCON AND NEURAL NETWORK GRAVITY MODELS JC Sanchez, JR Martin | | |
| Empirical Prediction of Wave Spectrum for Wind-Generated Gravity Waves AF Rauch, V Timmins, JRI Martin Twentieth Coastal Engineering Conference, 51-60, 0 | | |
