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.
Cheng, C., Han, B., Maddix, D.C., Ansari, A.F., Stuart, A., Mahoney, M.W., Wang, Y., (2024). "Hard Constraint Guided Flow Matching for Gradient-free Generation of PDE Solutions." Technical Report, Preprint arXiv:2412.01786, Under Review.
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.
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.
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.
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.
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.
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.
Talk at Foundation Models for Science: Progress, Opportunities, and Challenges, NeurIPS 2024 Workshop, Vancouver, Canada
Talk at Institute of Computational and Mathematical Engineering (ICME), Stanford University
Talk at UC Berkeley Mathematics Department, University of California, Berkeley
Talk at Ansys Monthly Seminar, Paris, France
Talk at ICERM Workshop on The Industrialization of SciML, Brown University
Talk at CS294 Graduate Course on Physics Inspired Deep Learning, University of California, Berkeley
Talk at 2nd AI for Good Webinar Series: AI for Earth and Sustainability Science, Virtual
Talk at Amazon Science, Virtual
Talk at Machine Learning and Dynamical Systems Seminar, Alan Turing Institute, London, England
Talk at Daves Avenue Elementary School Science Fair, Los Gatos, CA
Talk at Computational Math in Industry and Beyond Seminar Series (CME 500), Stanford, CA
Talk at International Conference on Machine Learning (ICML) Time Series Workshop, Long Beach, CA
Talk at SIAM Conference on Mathematical and Computational Issues in the Geosciences 2017, Erlangen, Germany
Talk at Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA