Danielle Maddix Robinson

Danielle Maddix Robinson

Senior Applied Scientist, AWS AI Labs, PhD in Computational and Mathematical Engineering, Stanford University

Publications

For a full list of my publications please see: my Google Scholar profile.

Selected Publications

Early Warning of Complex Climate Risk with Integrated Artificial Intelligence

Published in Nature Communications, 2024

Recommended citation: Reichstein, M., Benson, V., Blunk, J., Camps-Valls, G., Creuzig, F., Fearnley, C., Han, B., Kornhuber, K., , Rahaman, N., Schölkopf, B., Tárraga, J.M., Vinuesa, R., Dall, K., Denzler, J., Frank, D., Martini, G., Nganga, N., Maddix, D.C., Weldemariam, K., (2024). "Early Warning of Complex Climate Risk with Integrated Artificial Intelligence." Nature Communications, Accepted for Publication.

DrivAerML: High-Fidelity Computational Fluid Dynamics Dataset for Road-Car External Aerodynamics

Published in Technical Report, Preprint arXiv:2408.11969, 2024

Recommended citation: Ashton, N., Mockett, C., Fuchs, M., Fliessbach, L., Hetmann, H., Knacke, T., Schönwald, N., Skaperdas, V., Fotiadis, G., Walle, A., Hupertz, B. Maddix, D.C., Yu, P., (2024). "DrivAerML: High-Fidelity Computational Fluid Dynamics Dataset For Road-Car External Aerodynamics." Technical Report, Preprint arXiv:2408.11969, Under Review. https://arxiv.org/pdf/2408.11969

WindsorML–High-Fidelity Computational Fluid Dynamics Dataset For Automotive Aerodynamics

Published in Proceedings of the 38th Conference of Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track, 2024

Recommended citation: Ashton, N., Angel, J.B., Ghate, A.S., Kenway, G.K.W., Wong, M.L., Kiris, C., Walle, A., Maddix, D.C., Page, G., (2024). "WindsorML: High-Fidelity Computational Fluid Dynamics Dataset For Automotive Aerodynamics." Proceedings of the 38th Conference of Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track. https://arxiv.org/pdf/2407.19320

Comparing and Contrasting Deep Learning Weather Prediction Backbones on Navier-Stokes and Atmospheric Dynamics

Published in Technical Report, Preprint arXiv:2407.14129, 2024

Shorter version on Navier Stokes dynamics accepted at the ICLR 2024 Workshop on AI4DifferentialEquations In Science.

Recommended citation: Karlbauer, M., Maddix, D.C., Ansari, A.F., Han, B., Gupta, G., Wang, Y., Stuart, A., Mahoney, M.W., (2024). "Comparing and Contrasting Deep Learning Weather Prediction Backbones on Navier-Stokes and Atmospheric Dynamics." Technical Report, Preprint arXiv:2407.14129, Under Review. https://arxiv.org/abs/2407.14129

Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs

Published in Proceedings of the 41st International Conference on Machine Learning (ICML), 2024

Our Operator-ProbConserv code is on the amazon-science github.

Recommended citation: Mouli, S.C., Maddix, D.C., Alizadeh, S., Gupta, G., Wang, Y., Stuart, A., Mahoney, M.W. (2024). "Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs." Proceedings of the 41st International Conference on Machine Learning (ICML), PMLR. 235. https://arxiv.org/abs/2403.10642

Chronos: Learning the Language of Time Series

Published in Transactions on Machine Learning Research (TMLR), 2024

Our Chronos code is on the amazon-science github.

Recommended citation: Ansari, F.A., Stella, L., Turkmen, C., Zhang, X., Mercado, P., Shen, H., Shchur, O., Rangapuram, S.S., Arango, S.A., Kapoor, S., Zschiegner, J., Maddix, D.C., et al. (2024). "Chronos: Learning the Language of Time Series." Transactions on Machine Learning Research (10/2024). https://www.stat.berkeley.edu/~mmahoney/pubs/2619_Chronos_Learning_the_Lang.pdf

PreDiff: Precipitation Nowcasting with Latent Diffusion Models

Published in Proceedings of the 37th Conference of Neural Information Processing Systems (NeurIPS), 2023

Our PreDiff code is available on github.

Recommended citation: Gao, Z., Shi, X., Han, B., Wang, H., Jin, X., Maddix, D.C., Zhu, Y., Li, M., Wang, Y. (2023). "PreDiff: Precipitation Nowcasting with Latent Diffusion Models." Proceedings of the 37th Conference of Neural Information Processing Systems (NeurIPS). https://proceedings.neurips.cc/paper_files/paper/2023/file/f82ba6a6b981fbbecf5f2ee5de7db39c-Paper-Conference.pdf

Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting

Published in Proceedings of the 40th International Conference on Machine Learning (ICML), 2023

Recommended citation: Hasson, H., Maddix, D.C., Wang, Y., Park, Y., Gupta, G., (2023). "Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting." Proceedings of the 40th International Conference on Machine Learning (ICML), PMLR. 202:12616-12632. https://proceedings.mlr.press/v202/hasson23a/hasson23a.pdf

Guiding Continuous Operator Learning through Physics-based boundary constraints

Published in Proceedings of the International Conference on Learning Representations (ICLR), 2023

Our Boundary enforcing Operator Network (BOON) code is on the amazon-science github.

Recommended citation: Saad, N.*, Gupta, G.*, Alizadeh, S., Maddix, D.C. (2023). "Guiding Continuous Operator Learning through Physics-based boundary constraints." Proceedings of the International Conference on Learning Representations (ICLR). https://www.amazon.science/publications/guiding-continuous-operator-learning-through-physics-based-boundary-constraints

Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting

Published in Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS), 2022

Our Incremental Quantile Function (IQF) code is incorporated into the MQ-CNN Estimator in GluonTS.

Recommended citation: Park, Y., Maddix, D.C., Aubet, FX., Kan, K., Gasthaus, J., Wang, Y. (2022). "Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting." Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR. 151:8127-8150. https://proceedings.mlr.press/v151/park22a.html

Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems

Published in Proceedings of the 3rd Conference on Learning for Dynamics and Control (L4DC), 2021

Our AutoODE code is on github.

Recommended citation: Wang, R., Maddix, D.C., Faloutsos, C., Wang, Y., Yu, R. (2021). "Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems." Proceedings of the 3rd Conference on Learning for Dynamics and Control (L4DC), PMLR. 144:385-398. http://proceedings.mlr.press/v144/wang21a/wang21a.pdf

GluonTS: Probabilistic and Neural Time Series Modeling in Python

Published in Proceedings of the 39th International Conference on Machine Learning (ICML), 2020

GluonTS Code

Recommended citation: Alexandrov, A., Benidis, K., Bohlke-Schneider, M., Flunkert, V., Gasthaus, J., Januschowski, T., Maddix, D.C., Rangapuram, S., Salinas, D., Schulz, J., Stella, L., Türkmen, A.C., Wang, Y. (2020). "GluonTS: Probabilistic and Neural Time Series Modeling in Python." Journal of Machine Learning Research (JMLR). 21(116):1-6. https://www.jmlr.org/papers/v21/19-820.html

Advanced Fluid Reduced Order Models for Compressible Flow

Published in Sandia National Laboratories Report, Sand No. 2017-10335, 2017

Chapter 5: Structure preservation in finite volume ROMs via physics-based constraints

Recommended citation: Tezaur, I.K., Fike, J., Carlberg, K., Barone, M., Maddix, D.C., Mussoni, E., Balajewicz, M. (2017). "Advanced Fluid Reduced Order Models for Compressible Flow." Sandia National Laboratories Report, Sand No. 2017-10335. https://www.osti.gov/servlets/purl/1395816