| 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 | 10 | 2017 |
| 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 | 1 | 2024 |
| AI4DifferentialEquations In Science DC Maddix, S Alizadeh, B Han, AS Krishnapriyan, N Ashton, V Benson, ... ICLR 2024 Workshops, 0 | | |
| AutoODE: Bridging Physics-based and Data-driven modeling for COVID-19 Forecasting R Wang, D Maddix, C Faloutsos, Y Wang, R Yu | 1 | 2020 |
| 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 | 54 | 2021 |
| 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 | 29 | 2024 |
| Comparing and Contrasting Deep Learning Weather Prediction Backbones on Navier-Stokes and Atmospheric Dynamics M Karlbauer, DC Maddix, AF Ansari, B Han, G Gupta, Y Wang, A Stuart, ... arXiv preprint arXiv:2407.14129, 2024 | | 2024 |
| Cross-Frequency Time Series Meta-Forecasting M Van Ness, H Shen, H Wang, X Jin, DC Maddix, K Gopalswamy arXiv preprint arXiv:2302.02077, 2023 | 1 | 2023 |
| Deep factors for forecasting Y Wang, A Smola, D Maddix, J Gasthaus, D Foster, T Januschowski International conference on machine learning, 6607-6617, 2019 | 209 | 2019 |
| Deep factors with gaussian processes for forecasting DC Maddix, Y Wang, A Smola arXiv preprint arXiv:1812.00098 10, 2018 | 44 | 2018 |
| 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 | 262* | 2018 |
| Diagnosing malignant versus benign breast tumors via machine learning techniques in high dimensions DC Maddix Stanford Univ., Stanford, CA, USA, Tech. Rep, 2014 | 3 | 2014 |
| 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 | 69 | 2022 |
| 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 | 25 | 2022 |
| 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 | 346* | 2020 |
| GOPHER: Categorical probabilistic forecasting with graph structure via local continuous-time dynamics KA Wang, D Maddix, Y Wang I (Still) Can't Believe It's Not Better! Workshop at NeurIPS 2021, 80-85, 2022 | | 2022 |
| Guiding continuous operator learning through Physics-based boundary constraints N Saad, G Gupta, S Alizadeh, DC Maddix International Conference on Learning Representations, 2023 | 17 | 2023 |
| Investigating the Effects of MINRES with Local Reorthogonalization DC Maddix | | 2015 |
| 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 |
| 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 | 35 | 2024 |
