Publications

MITRA: Mixed Synthetic Priors for Enhancing Tabular Foundation Models

Published in Proceedings of the 39th Conference of Neural Information Processing Systems (NeurIPS), 2025

Recommended citation: Zhang, X., Maddix, D.C., Yin, J., Erickson, N., Ansari, A.F., Han, B., Zhang, S., Akoglu, L., Faloutsos, C., Mahoney, M.W., Wang, Y., Hu, C., Rangwala, H., Karypis, G., Wang, B. (2025). "MITRA: Mixed Synthetic Priors for Enhancing Tabular Foundation Models. " Proceedings of the 39th Conference of Neural Information Processing Systems (NeurIPS), Accepted. https://arxiv.org/abs/2510.21204

Understanding the Implicit Biases of Design Choices for Time Series Foundation Models

Published in Technical Report, Preprint arXiv:2510.19236, 2025

Recommended citation: Yu, A., Maddix, D.C., Han, B., Zhang, X., Ansari, A.F., Shchur, O., Faloutsos, C., Wilson, A.G., Mahoney, M.W., Wang, Y., (2025). "Understanding the Implicit Biases of Design Choices for Time Series Foundation Models. " Technical Report, Preprint arXiv:2510.19236, Under Review. https://arxiv.org/abs/2510.19236

Understanding Transformers for Time Series: Rank Structure, Flow-of-ranks, and Compressibility

Published in Technical Report, Preprint arXiv:2510.03358, 2025

Recommended citation: Yu, A., Maddix, D.C., Han, B., Zhang, X., Ansari, A.F., Shchur, O., Faloutsos, C., Wilson, A.G., Mahoney, M.W., Wang, Y., (2025). "Understanding Transformers for Time Series: Rank Structure, Flow-of-ranks, and Compressibility. " Technical Report, Preprint arXiv:2510.03358, Under Review. https://arxiv.org/abs/2510.03358

End-to-End Probabilistic Framework for Learning with Hard Constraints

Published in Technical Report, Preprint arXiv:2506.07003, 2025

Recommended citation: Utkarsh, Maddix, D.C., Ma, R., Mahoney, M.W., Wang, Y., (2025). "End-to-End Probabilistic Framework for Learning with Hard Constraints." Technical Report, Preprint arXiv:2506.07003, Under Review. https://arxiv.org/abs/2506.07003

Gradient-Free Generation for Hard-Constrained Systems

Published in The Thirteenth International Conference on Learning Representations (ICLR), 2025

Recommended citation: Cheng, C., Han, B., Maddix, D.C., Ansari, A.F., Stuart, A., Mahoney, M.W., Wang, Y., (2025). "Gradient-Free Generation for Hard-Constrained Systems." The Thirteenth International Conference on Learning Representations (ICLR). https://openreview.net/pdf?id=teE4pl9ftK

Early Warning of Complex Climate Risk with Integrated Artificial Intelligence

Published in Nature Communications, 2025

Recommended citation: Reichstein, M., Benson, V., Blunk, J., Camps-Valls, G., Creuzig, F., Fearnley, C.J., 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., (2025). "Early Warning of Complex Climate Risk with Integrated Artificial Intelligence." Nature Communications 16, 2564. https://rdcu.be/eefiY

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., Schonwald, N., Skaperdas, V., Fotiadis, G., Walle, A., Hupertz, B., Maddix, D.C., (2024). "DrivAerML: High-Fidelity Computational Fluid Dynamics Dataset for Road-Car External Aerodynamics." Technical Report, Preprint arXiv:2408.11969, Under Review. https://arxiv.org/abs/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) Track on Datasets and Benchmarks, 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) Track on Datasets and Benchmarks, 37:27823-37835. https://proceedings.neurips.cc/paper_files/paper/2024/file/42a59a5f35b1b3c3fd648397c88a7164-Paper-Datasets_and_Benchmarks_Track.pdf

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. 235:36372-36418. https://proceedings.mlr.press/v235/mouli24a.html

Transferring Knowledge from Large Foundation Models to Small Downstream Tasks

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

Recommended citation: Qiu, S., Han, B, Maddix, D.C., Zhang, S., Wang, Y., Wilson, A.G. (2024). "Transferring Knowledge from Large Foundation Models to Small Downstream Tasks." Proceedings of the 41st International Conference on Machine Learning (ICML), PMLR. 235:41644-41657. https://proceedings.mlr.press/v235/qiu24d.html

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

Machine Learning for Road Vehicle Aerodynamics Simulation

Published in Society of Automotive Engineers (SAE) Technical Paper, 2024

Recommended citation: Ananthan, V., Ashton, N., Chadwick, N., Lizarraga, M., Maddix, D.C., et al. (2024). "Machine Learning for Road Vehicle Aerodynamics Simulation." Society of Automotive Engineers (SAE) Technical Paper. https://www.sae.org/publications/technical-papers/content/2024-01-2529/

Learning Physical Models that Can Respect Conservation Laws

Published in Physica D: Nonlinear Phenomena, 2024

Our ProbConserv code is on the amazon-science github.

Recommended citation: Hansen, D.*, Maddix, D.C.*, Alizadeh, S., Gupta, G., Mahoney, M.W. (2024). "Learning Physical Models that Can Respect Conservation Laws." Physica D: Nonlinear Phenomena, 457 (133952), (*Equal contributions). https://doi.org/10.1016/j.physd.2023.133952

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

Learning Physical Models that Can Respect Conservation Laws

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

Recommended citation: Hansen, D., Maddix, D.C., Alizadeh, S., Gupta, G., Mahoney, M.W. (2023). "Learning Physical Models that Can Respect Conservation Laws." Proceedings of the 40th International Conference on Machine Learning (ICML), PMLR. 202:12469-12510. http://proceedings.mlr.press/v202/hansen23b/hansen23b.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

Deep Learning For Time Series Forecasting: Tutorial and Literature Survey

Published in ACM Computing Surveys, 2022

Recommended citation: Benidis, K., Rangapuram, S., Flunkert V., Wang, Y., Maddix, D.C., Türkmen, C., Gasthaus, J., Bohlke-Schneider, M., Salinas, D., Stella, L., Aubet, FX., Callot, L, Januschowski, T. (2022). "Deep Learning for Time Series Forecasting: Tutorial and Literature Survey." ACM Computing Surveys. 55(6):1-36. https://arxiv.org/pdf/2004.10240

Domain Adaptation for Time Series Forecasting via Attention Sharing

Published in Proceedings of the 39th International Conference on Machine Learning (ICML), 2022

Recommended citation: Jin, X., Park, Y., Maddix, D.C., Wang, H., Wang, Y. (2022). "Domain Adaptation for Time Series Forecasting via Attention Sharing." Proceedings of the 39th International Conference on Machine Learning (ICML), PMLR. 162:10280-10297. https://proceedings.mlr.press/v162/jin22d/jin22d.pdf

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

Deep Factors for Forecasting

Published in Proceedings of the 36th International Conference on Machine Learning (ICML), 2019

Our Deep Factor code is in GluonTS.

Recommended citation: Wang, Y., Smola, A., Maddix, D.C., Gasthaus, J., Foster, D. (2019). "Deep Factors for Forecasting." Proceedings of the 39th International Conference on Machine Learning (ICML), PMLR. 97:6607-6617. http://proceedings.mlr.press/v97/wang19k/wang19k.pdf

Numerical Artifacts in the discontinuous Generalized Porous Medium Equation: How to avoid spurious temporal oscillations

Published in Journal of Computational Physics, 2018

Recommended citation: Maddix, D.C., Sampaio, L., Gerritsen, M. (2018). "Numerical Artifacts in the discontinuous Generalized Porous Medium Equation: How to avoid spurious temporal oscillations." Journal of Computational Physics. 368:277-298. https://arxiv.org/abs/1712.00132

Numerical Artifacts in the Generalized Porous Medium Equation: Why harmonic averaging itself is not to blame

Published in Journal of Computational Physics, 2018

Recommended citation: Maddix, D.C., Sampaio, L., Gerritsen, M. (2018). "Numerical Artifacts in the Generalized Porous Medium Equation: Why harmonic averaging itself is not to blame." Journal of Computational Physics. 361:280-298. https://arxiv.org/abs/1709.02581

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