| GluonTS: Probabilistic and Neural Time Series Modeling in Python A Alexandrov, K Benidis, M Bohlke-Schneider, V Flunkert, J Gasthaus, ... Journal of Machine Learning Research 21 (116), 1-6, 2020 | 397* | 2020 |
| Deep Learning for Time Series Forecasting: Tutorial and Literature Survey K Benidis, SS Rangapuram, V Flunkert, Y Wang, D Maddix, C Turkmen, ... ACM Computing Surveys (CSUR), 2018 | 345* | 2018 |
| Deep factors for forecasting Y Wang, A Smola, D Maddix, J Gasthaus, D Foster, T Januschowski International conference on machine learning, 6607-6617, 2019 | 238 | 2019 |
| Chronos: Learning the language of time series AF Ansari, L Stella, C Turkmen, X Zhang, P Mercado, H Shen, O Shchur, ... arXiv preprint arXiv:2403.07815, 2024 | 178 | 2024 |
| Domain adaptation for time series forecasting via attention sharing X Jin, Y Park, D Maddix, H Wang, Y Wang International Conference on Machine Learning, 10280-10297, 2022 | 92 | 2022 |
| Bridging physics-based and data-driven modeling for learning dynamical systems R Wang, D Maddix, C Faloutsos, Y Wang, R Yu Learning for dynamics and control, 385-398, 2021 | 65 | 2021 |
| Prediff: Precipitation nowcasting with latent diffusion models Z Gao, X Shi, B Han, H Wang, X Jin, D Maddix, Y Zhu, M Li, YB Wang Advances in Neural Information Processing Systems 36, 2024 | 52 | 2024 |
| Deep factors with gaussian processes for forecasting DC Maddix, Y Wang, A Smola arXiv preprint arXiv:1812.00098 10, 2018 | 51 | 2018 |
| Learning Physical Models that Can Respect Conservation Laws D Hansen, DC Maddix, S Alizadeh, G Gupta, MW Mahoney Physica D: Nonlinear Phenomena 457 (133952), 2024 | 50 | 2024 |
| First De-Trend then Attend: Rethinking Attention for Time-Series Forecasting X Zhang, X Jin, K Gopalswamy, G Gupta, Y Park, X Shi, H Wang, ... NeurIPS'22 Workshop on All Things Attention: Bridging Different Perspectives …, 2022 | 43 | 2022 |
| Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting Y Park, D Maddix, FX Aubet, K Kan, J Gasthaus, Y Wang International Conference on Artificial Intelligence and Statistics 151, 8127 …, 2022 | 39 | 2022 |
| Guiding continuous operator learning through Physics-based boundary constraints N Saad, G Gupta, S Alizadeh, DC Maddix International Conference on Learning Representations, 2023 | 25 | 2023 |
| Numerical Artifacts in the Generalized Porous Medium Equation: Why Harmonic Averaging Itself Is Not to Blame D Maddix, M Gerritsen, L Sampaio Journal of Computational Physics 361, 280-298, 2018 | 13 | 2018 |
| Advanced Fluid Reduced Order Models for Compressible Flow. IK Tezaur, JA Fike, KT Carlberg, MF Barone, D Maddix, EE Mussoni, ... Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2017 | 11 | 2017 |
| Numerical artifacts in the discontinuous Generalized Porous Medium Equation: How to avoid spurious temporal oscillations DC Maddix, L Sampaio, M Gerritsen Journal of Computational Physics 368, 277-298, 2018 | 9 | 2018 |
| Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs SC Mouli, DC Maddix, S Alizadeh, G Gupta, A Stuart, MW Mahoney, ... arXiv preprint arXiv:2403.10642, 2024 | 6 | 2024 |
| DrivAerML: High-fidelity computational fluid dynamics dataset for road-car external aerodynamics N Ashton, C Mockett, M Fuchs, L Fliessbach, H Hetmann, T Knacke, ... arXiv preprint arXiv:2408.11969, 2024 | 5 | 2024 |
| Learning Dynamical Systems Requires Rethinking Generalization R Wang, D Maddix, C Faloutsos, W Yuyang, R Yu Interpretable Inductive Bias and Physically Structured Learning NeurIPS Workshop, 2020 | 4 | 2020 |
| AhmedML: High-fidelity computational fluid dynamics dataset for incompressible, low-speed bluff body aerodynamics N Ashton, DC Maddix, S Gundry, PM Shabestari arXiv preprint arXiv:2407.20801, 2024 | 3 | 2024 |
| Theoretical guarantees of learning ensembling strategies with applications to time series forecasting H Hasson, DC Maddix, B Wang, G Gupta, Y Park International Conference on Machine Learning, 12616-12632, 2023 | 3 | 2023 |
