Gwak, M., Hsu, H.H, Maddix, D.C., N. Benjamin Erichson, (2026). "LyapuFlow: Controlling Generative Flows with Lyapunov Feedback for Inverse Problems." Technical Report, Preprint arXiv:2610.04326.
Tan, S., Rangwala, H., Maddix, D.C., et al., (2026). "Threshold-Aware Conformal Routing." Technical Report, Preprint arXiv:2610.02487.
Tao, Y., Zhang, X., Liu, X., Han, B., Maddix, D.C., et al., (2024). "Mitra-v2 Technical Report." Technical Report, Preprint arXiv:2609.04540.
Yu, A., Maddix, D.C., Han, B., Zhang, X., Ansari, A.F., Shchur, O., Faloutsos, C., Wilson, A.G., Mahoney, M.W., Wang, Y., (2026). "Understanding Transformers for Time Series: Rank Structure, Flow-of-ranks, and Compressibility. " The Fourteenth International Conference on Learning Representations (ICLR).
Yu, A., Maddix, D.C., Han, B., Zhang, X., Ansari, A.F., Shchur, O., Faloutsos, C., Wilson, A.G., Mahoney, M.W., Wang, Y., (2026). "Understanding the Implicit Biases of Design Choices for Time Series Foundation Models. " The Fourteenth International Conference on Learning Representations (ICLR).
Utkarsh, Maddix, D.C., Ma, R., Mahoney, M.W., Wang, Y., (2026). "End-to-End Probabilistic Framework for Learning with Hard Constraints." The Fourteenth International Conference on Learning Representations (ICLR).
Karlbauer, M., Maddix, D.C., Ansari, A.F., Han, B., Gupta, G., Wang, Y., Stuart, A., Mahoney, M.W., (2026). "Comparing and Contrasting Deep Learning Weather Prediction Backbones on Navier-Stokes and Atmospheric Dynamics." Data Science in Science, 5(1).
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).
Ansari, A.F., Shchur, O., Küken, J., Auer, A., Han, B., Mercado, P., Rangapuram, S.S., Shen, H., Stella, L., Zhang, X., Goswami, M., Kapoor, S., Maddix, D.C., et al., (2024). "Chronos-2: From Univariate to Universal Forecasting." Technical Report, Preprint arXiv:2510.15821.
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).
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.
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.
Ashton, N., Maddix, D.C., Gundry, S., Shabestari, P.M., (2024). "AhmedML: High-Fidelity Computational Fluid Dynamics Dataset for Incompressible, Low-Speed Bluff Body Aerodynamics." Technical Report, Preprint arXiv:2407.20801.
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.
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.
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.
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).
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.
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).
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).
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.
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.
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).
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.
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.
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.
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.
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.
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.
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.
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.
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.
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