Sea Ice Modeling and Data Assimilation (SIMDA)

Integrated Foundations of Sensing, Modeling, and Data Assimilation for Sea Ice Prediction
Department of Defense Multidisciplinary University Research Initiatives program through the Office of Naval Research

Publications and preprints

  1. Chen, Z., Gelb, A., Lee, Y., “Designing Neural Networks for Hyperbolic Conservation Laws”, Submitted, (2022).
  2. Parno, M., Rubio, P-B, Sharp, D., Brennan, M., Baptista, R., Bonart H., and Marzouk, Y., “MParT: Monotone Parameterization Toolkit,” Submitted, (2022).
  3. Baptista, R., Ramgraber, M., McLaughlin, D., Marzouk, Y. “Ensemble transport smoothing. Part I: unified framework.” Submitted (2022).
  4. Max Ramgraber, Ricardo Baptista, Dennis McLaughlin, Youssef Marzouk. “Ensemble transport smoothing. Part II: nonlinear updates.” Submitted (2022).
  5. Churchill, V., and Gelb, A. “Sampling-based spotlight SAR image reconstruction from phase history data for speckle reduction and uncertainty quantification”, SIAM Journal on Uncertainty Quantification, 10:3 pp. 1225-1249 (2022).
  6. Zhang, J., Gelb, A., and Scarnati T., “Empirical Bayesian Inference using Joint Sparsity”, SIAM Journal on Uncertainty Quantification, 10:2 745-774 (2022).
  7. Li, T., Gelb, A., and Lee, Y. “Improving numerical accuracy for the viscous-plastic formulation of sea ice” Preprint, submitted in 2022. https://arxiv.org/abs/2206.10061
  8. Glaubitz, J., Gelb, A., and Song, G, “Generalized sparse Bayesian learning and application to image reconstruction”, arxiv.org:2201.07061, 2022
  9. Glaubitz, J., and Reeger, J. “Towards stability of radial basis function based cubature formulas” Preprint, submitted in 2021. https://arxiv.org/abs/2108.06375
  10. Glaubitz, J., Nordström, J., and Öffner, P. “Summation-by-parts operators for general function spaces” Preprint, submitted in 2022. https://arxiv.org/abs/2203.05479
  11. Glaubitz, J., Nordström, J., and Öffner, P. “Energy-stable global radial basis function methods on summation-by-parts form” Preprint, submitted in 2022. https://arxiv.org/abs/2204.03291
  12. Han, J., and Lee, Y. “A Neural Network Approach for Homogenization of Multiscale Problems” Preprint, submitted in 2022. https://arxiv.org/abs/2206.02032
  13. Han, J., and Lee, Y. “Hierarchical Learning to Solve Partial Differential Equations Using Physics-Informed Neural Networks” Preprint, submitted in 2021. https://arxiv.org/abs/2112.01254
  14. Han, J., and Lee, Y. “Inhomogeneous Regularization with Limited and Indirect Data” Preprint, submitted in 2021. https://arxiv.org/abs/2108.01703
  15. Churchill, V., and Gelb, “Estimation and Uncertainty Quantification for Piecewise Smooth Signal Recovery”, Journal of Computational Mathematics, accepted.
  16. Lee, Y., Sampling Error Correction in Ensemble Kalman Inversion, arXiv:2105.11341
  17. Glaubitz, J. “Construction and application of provable positive and exact cubature formulas”, IMA Journal of Numerical Analysis, (2022), https://doi.org/10.1093/imanum/drac017
  18. Xiao, Y., Glaubitz, J., Gelb, A., and Song, G. “Sequential image recovery from noisy and under-sampled Fourier data” Journal of Scientific Computing. 91, no. 3 (2022), https://doi.org/10.1007/s10915-022-01850-7
  19. Parno, J., Polashenski, C., Parno, M., Nelsen, T., Mahoney, A., and Song, A. “Observations of Stress‐Strain in Drifting Sea Ice at Floe Scale” Journal of Geophysical Research: Oceans. 127, no. 5 (2021), https://doi.org/10.1029/2021JC017761
  20. West, B., O’Connor, D., Parno, M., Krackow, M., and Polashenski, C. “Bonded discrete element simulations of sea ice with non‐local failure: Applications to Nares Strait” Journal of Advances in Modeling Earth Systems. 14, no. 6 (2021), https://doi.org/10.1029/2021MS002614
  21. Lee, Y. “l_p Regularization for Ensemble Kalman Inversion” SIAM Journal on Scientific Computing. 43, no. 5 (2021), https://doi.org/10.1137/20M1365168

Workshops, Conferences, and Seminar talks

  1. Lee, Y., Center for Data Science and Machine Learning seminar (virtual presentation), National University of Singapore, Nov 10, 2022.
  2. Han, J., New Ideas in Computational Inverse Problems, BIRS, Oct 28, 2022.
  3. Marzouk, Y., New Ideas in Computational Inverse Problems, BIRS, Oct 24, 2022.
  4. Han, J., Mathematical Sciences Colloquium, Univ of Mass at Lowell, Lowell, MA, Oct 19, 2022.
  5. Lee, Y., AMS East Sectional Meeting, Amherst, MA, Oct 1, 2022.
  6. Marzouk, Y., “Measure transport and dimension reduction for simulation-based inference.” Model Reduction and Surrogate Modeling (MORE). Berlin, September 2022. Invited plenary.
  7. Lee, Y., SIAM Mathematics of Data Science. San Diego, CA, Sep 28, 2022.
  8. Marzouk, Y., “Likelihood-free Bayesian inference via transportation of measure.” USACM Thematic Conference on Uncertainty Quantification for Machine Learning Integrated Physics Modeling (UQ-MLIP). Washington, DC. August 2022.
  9. Marzouk, Y., “Transport methods for simulation-based Bayesian inference.” Joint Statistics Meetings. Washington, DC. August 2022.
  10. Lee, Y., International Conference on Machine Learning and PDEs, Seoul, South Korea, Aug 28, 2022.
  11. Lee, Y., Applied Mathematics seminar, Ewha Womans University, Jul 28, 2022.
  12. Li, T., Gelb, A., Lee, Y., NAHOMCon 22, San Diego, CA, Jul 18, 2022.
  13. Marzouk, Y., “Conditional sampling and joint dimension reduction, with application to data assimilation.” University of Potsdam, SFB 1294 Data Assimilation. Berlin. July 2022.
  14. Li, T., Gelb, A., Lee, Y., SIAM Mathematics of Planet Earth, Pittsburgh, PA, July 2022.
  15. Marzouk, Y., “Transport methods for nonlinear ensemble filtering and smoothing.” International Symposium on Data Assimilation. Fort Collins, CO. June 2022. Invited keynote.
  16. Marzouk, Y., “Transport methods for simulation-based inference and data assimilation.” Cantab Capital Institute for the Mathematics of Information, Uncertainty Quantification : Recent Advances in the Mathematics of Information. Invited. Cambridge, UK (virtual). May 2022.
  17. Marzouk, Y., “Transport methods for simulation-based inference and data assimilation.” UC-Berkeley Institute for Data Science, BIDS Machine Learning and Science Forum. Berkeley, CA (virtual). May 2022.
  18. Lindbloom, J., Gelb, A., and Parno, M. “Multiplicative Denoising with Uncertainty Quantification for Synthetic Aperture Radar Imaging” SIAM Conference on Uncertainty Quantification. Atlanta, GA, Apr 14, 2022.
  19. Marzouk, Y., “Transport methods for simulation-based inference and data assimilation.” SIAM Conference on Uncertainty Quantification (UQ22). Atlanta, GA. April 2022. Invited plenary.
  20. Maurais, A., Marzouk, Y., Peherstorfer, B., and Alsup, T. “Multifidelity Covariance Estimation Three Ways” SIAM Conference on Uncertainty Quantification. Atlanta, GA, Apr 14, 2022.
  21. Rubio, P.-B., and Marzouk, Y. “Transport-Based Offline/Online Approach for Sequential Bayesian Inference” SIAM Conference on Uncertainty Quantification. Atlanta, GA, Apr 15, 2022.
  22. Parno, M., Dowdle, C., and Rubio, P.-B. “Localized Transport Maps for Non-Gaussian Random Fields with Applications in Sea Ice” SIAM Conference on Uncertainty Quantification. Atlanta, GA, Apr 15, 2022.
  23. Han, J. and Lee, Y. “Inhomogeneous Regularization with Limited and Indirect Data” SIAM Conference on Imaging Sciences. virtual, Mar 21, 2022.
  24. Marzouk, Y., University of Nottingham, School of Mathematical Sciences, Statistics and Probability seminar. Nottingham, UK (virtual). March 2022.
  25. Glaubitz, J., Gelb, A., and Song, G. “Sparse Bayesian image reconstruction: Towards a unified approach” SIAM Conference on Imaging Science. virtual, Mar 24, 2022.
  26. Parno, M., Zhou, B., and Ronan, J. “A Practical Tour of Computational Optimal Transport” DoMSS seminar. Arizona State University, Feb 21, 2022.
  27. Marzouk, Y., Texas A&M University, Department of Industrial and Systems Engineering. College Station, Texas (virtual). February 2022.
  28. Lee, Y. “Hierarchical Learning to Solve Partial Differential Equations Using Physics-Informed Neural Networks” Applied and Computational Mathematics seminar. UC Riverside, Feb 2, 2022.
  29. Lee, Y. “Hierarchical Learning to Solve Partial Differential Equations Using Physics-Informed Neural Networks” Machine Learning+X seminar. Brown University, Jan 7, 2022.
  30. Glaubitz, J. and Gelb, A. “Sparse Bayesian learning for image reconstruction with uncertainty quantification” AFOSR 2022 Annual EM Portfolio Review. virtual, Jan 4, 2022.
  31. Parno, M., Polashenski, C., Parno, J., and O’Connor, D. “Inverse Problems for Characterizing Floe Scale Sea Ice Dynamics” AGU Fall Meeting. New Orleans, LA, Dec 13, 2021.
  32. Han, J. “Hierarchical Learning to Solve Partial Differential Equations Using Physics-Informed Neural Networks” National Institute of Mathematical Sciences Seminar. virtual, Dec 16, 2021.
  33. Lee, Y. “Finding a needle in sand beach” Applied Mathematics Seminar. Seoul National University, Dec 15, 2021.
  34. Han, J. “Hierarchical Learning to Solve Partial Differential Equations Using Physics-Informed Neural Networks” Applied Mathematics Seminar. Sungkyunkwan University, Dec 14, 2021.
  35. Lee, Y. “Finding a needle in sand beach” Applied Mathematics Seminar. Sungkyunkwan University, Dec 14, 2021.
  36. Lee, Y. “Finding a needle in sand beach” Applied Mathematics Seminar. Yonsei University, Dec 10, 2021.
  37. Han, J. “Hierarchical Learning to Solve Partial Differential Equations Using Physics-Informed Neural Networks” Applied Mathematics Seminar. Kyungpook National University, Dec 08, 2021.
  38. Lee, Y. “Finding a needle in sand beach” Applied Mathematics Seminar. Kyungpook National University, Dec 08, 2021.
  39. Lee, Y. “Finding a needle in sand beach” Mathematics Colloquium. UNIST, Dec 06, 2021.
  40. Han, J. “Hierarchical Learning to Solve Partial Differential Equations Using Physics-Informed Neural Networks” KSIAM Fall Meeting. Busan, South Korea, Dec 02, 2021.
  41. Marzouk, Y., Banff workshop, “Statistical Aspects of Nonlinear Inverse Problems.” Banff, Canada (virtual). November 2021.
  42. Parno, M. “When Models and Reality Collide: Model discrepancy in deterministic and Bayesian inverse problems” Clarkson Mathematics Colloquium. Clarkson University, Oct 18, 2021.
  43. Marzouk, Y., George Washington University, Center for Mathematics and Artificial Intelligence (CMAI) Colloquium (virtual). October 2021.
  44. Marzouk, Y., Centre International de Rencontres Mathematiques (CIRM) workshop, “On future synergies for stochastic and learning algorithms.” Marseille, France (virtual). September 2021.
  45. Marzouk, Y., CERN, Seminar on Machine Learning for Simulation. Geneva, Switzerland (virtual). September 2021.
  46. Marzouk, Y., University of Potsdam. SFB 1294 kickoff meeting (keynote). Potsdam, Germany (virtual). September 2021.
  47. Lee, Y. “Data Interpretation and Quantification for Inverse Problems” Image Analysis and Data Processing in Superresolution Microscopy. Prague, Czech, Sep 01, 2021.
  48. Glaubitz, J., and Gelb, A. “High order edge sensors with l1 regularization for enhanced discontinuous Galerkin methods” ICOSAHOM 2020. virtual, Jul 14, 2021.
  49. Marzouk, Y., RWTH Aachen University, Chair of Mathematics for Uncertainty Quantification, Seminar. Aachen, Germany (virtual). July 2021.
  50. Marzouk, Y., Bath-ICMS workshop on “Analytical and Geometric Approaches to Machine Learning.” Invited speaker. Bath, United Kingdon (virtual). July 2021.
  51. Marzouk, Y., Bernoulli-IMS 10th World Congress on Probability and Statistics. Invited session speaker. Seoul, Korea (virtual). July 2021.
  52. Marzouk, Y., SIAM Annual Meeting (AN21). Invited minisymposium (virtual). July 2021.
  53. Glaubitz, J., Öffner, P., and Gelb, A. “Least Squares Formulas - The Swiss Army Knife of Numerical Integration?” SIAM Annual Meeting. virtual, Jul 19, 2021.
  54. Parno, M., and Ronan, J. “Applications of Optimal Transport in Sea Ice Dynamics” SIAM Annual Meeting. virtual, Jul 20, 2021.
  55. Gelb, A. Empirical Bayesian Inference Using Joint Sparsity, Keynote Speaker, International Conference on Computational Science (virtual), Krakow, Poland, June 17, 2021.

Organizations

  1. Marzouk, Y., Co-organizer, workshop on “Mathematical foundations of data assimilation and inverse problems” at Foundations of Computational Mathematics (FoCM) 2023. Paris, France. June 2023.
  2. Marzouk, Y., Co-organizer, workshop on “Computational Challenges and Emerging Tools,” Isaac Newton Institute program on “The mathematical and statistical foundation of future data-driven engineering.” Cambridge, UK. April 2023.
  3. Friderick, C., Lee, Y., Ren, K., Yang, Y., “New Ideas in Computational Inverse Problems”, Banff International Research Station, Oct 24-28, 2022.
  4. Lee, Y., Parno, M., “Sea Ice Modeling and Data Assimilation”, Minisymposium at SIAM Mathematics of Planet Earth, Jul 14-15, 2022.
  5. Parno, M. “An Introduction to Sampling with Measure Transport” Tutorial at SIAM Annual Meeting. Pittsburgh, PA, Jul 13, 2022.
  6. Gelb, A., Glaubitz, J., and Song, G. “Sparse Bayesian learning for image reconstruction with uncertainty quantification” Minisymposium at SIAM Conference on Imaging Science. virtual, Mar 24, 2022.
  7. Marzouk, Y., Co-organizer, Oberwolfach workshop on “Data assimilation: mathematical foundations and applications.” Oberwolfach, Germany. February 2022.
  8. Lee, Y., Choi, M., “Scientific Computing and Machine Learning”, Minisymposium at KSIAM Fall Meeting, Dec 3, 2021.
  9. Parno, M. “Data Science, Remote Sensing, and Uncertainty Quantification for Sea Ice” Minisymposium at SIAM Annual Meeting. virtual, Jul 20, 2021.
  10. Marzouk, Y., Co-chair of organizing committee, 021 SIAM Conference on Mathematical and Computational Issues in the Geosciences (GS21). June 2021.
  11. Marzouk, Y., Organizing committee, Department of Energy Workshop on Data Reduction for Science. February 2021.

Thesis

  1. Xiao, Y. Sequential Image Recovery from Noisy and Under-sampled Fourier Data, PhD dissertation, Dartmouth College, 2022 Supervisor: Anne Gelb.
  2. Maurais, A. Multifidelity Covariance Estimation Three Ways Master’s Thesis, Massachusetts Institute of Technology, 2022 Supervisor: Youssef Marzouk
  3. Ronan J. New techniques in optimal transport, PhD dissertation, Dartmouth College, 2021 Supervisor: Anne Gelb.

Software

  1. SDOT2D: C++ library for semi-discrete optimal transport in 2D. M. Parno and J. Ronan
  2. MUQ: MIT Uncertainty Quantification Library, M. Parno, A. Davis, L. Seelinger, and Y. Marzouk
  3. MParT: Monotone Parameterization Toolbox, M Parno