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

- Chen, Z., Gelb, A., Lee, Y., “Designing Neural Networks for Hyperbolic Conservation Laws”, Submitted, (2022).
- Parno, M., Rubio, P-B, Sharp, D., Brennan, M., Baptista, R., Bonart H., and Marzouk, Y., “MParT: Monotone Parameterization Toolkit,” Submitted, (2022).
- Baptista, R., Ramgraber, M., McLaughlin, D., Marzouk, Y. “Ensemble transport smoothing. Part I: unified framework.” Submitted (2022).
- Max Ramgraber, Ricardo Baptista, Dennis McLaughlin, Youssef Marzouk. “Ensemble transport smoothing. Part II: nonlinear updates.” Submitted (2022).
- 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).
- Zhang, J., Gelb, A., and Scarnati T., “Empirical Bayesian Inference using Joint Sparsity”, SIAM Journal on Uncertainty Quantification, 10:2 745-774 (2022).
- 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
- Glaubitz, J., Gelb, A., and Song, G, “Generalized sparse Bayesian learning and application to image reconstruction”, arxiv.org:2201.07061, 2022
- Glaubitz, J., and Reeger, J. “Towards stability of radial basis function based cubature formulas” Preprint, submitted in 2021. https://arxiv.org/abs/2108.06375
- 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
- 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
- Han, J., and Lee, Y. “A Neural Network Approach for Homogenization of Multiscale Problems” Preprint, submitted in 2022. https://arxiv.org/abs/2206.02032
- 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
- Han, J., and Lee, Y. “Inhomogeneous Regularization with Limited and Indirect Data” Preprint, submitted in 2021. https://arxiv.org/abs/2108.01703
- Churchill, V., and Gelb, “Estimation and Uncertainty Quantification for Piecewise Smooth Signal Recovery”, Journal of Computational Mathematics, accepted.
- Lee, Y., Sampling Error Correction in Ensemble Kalman Inversion, arXiv:2105.11341
- 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
- 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
- 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
- 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
- Lee, Y. “l_p Regularization for Ensemble Kalman Inversion” SIAM Journal on Scientific Computing. 43, no. 5 (2021), https://doi.org/10.1137/20M1365168

- Lee, Y., Center for Data Science and Machine Learning seminar (virtual presentation), National University of Singapore, Nov 10, 2022.
- Han, J., New Ideas in Computational Inverse Problems, BIRS, Oct 28, 2022.
- Marzouk, Y., New Ideas in Computational Inverse Problems, BIRS, Oct 24, 2022.
- Han, J., Mathematical Sciences Colloquium, Univ of Mass at Lowell, Lowell, MA, Oct 19, 2022.
- Lee, Y., AMS East Sectional Meeting, Amherst, MA, Oct 1, 2022.
- Marzouk, Y., “Measure transport and dimension reduction for simulation-based inference.” Model Reduction and Surrogate Modeling (MORE). Berlin, September 2022. Invited plenary.
- Lee, Y., SIAM Mathematics of Data Science. San Diego, CA, Sep 28, 2022.
- 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.
- Marzouk, Y., “Transport methods for simulation-based Bayesian inference.” Joint Statistics Meetings. Washington, DC. August 2022.
- Lee, Y., International Conference on Machine Learning and PDEs, Seoul, South Korea, Aug 28, 2022.
- Lee, Y., Applied Mathematics seminar, Ewha Womans University, Jul 28, 2022.
- Li, T., Gelb, A., Lee, Y., NAHOMCon 22, San Diego, CA, Jul 18, 2022.
- Marzouk, Y., “Conditional sampling and joint dimension reduction, with application to data assimilation.” University of Potsdam, SFB 1294 Data Assimilation. Berlin. July 2022.
- Li, T., Gelb, A., Lee, Y., SIAM Mathematics of Planet Earth, Pittsburgh, PA, July 2022.
- Marzouk, Y., “Transport methods for nonlinear ensemble filtering and smoothing.” International Symposium on Data Assimilation. Fort Collins, CO. June 2022. Invited keynote.
- 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.
- 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.
- 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.
- Marzouk, Y., “Transport methods for simulation-based inference and data assimilation.” SIAM Conference on Uncertainty Quantification (UQ22). Atlanta, GA. April 2022. Invited plenary.
- Maurais, A., Marzouk, Y., Peherstorfer, B., and Alsup, T. “Multifidelity Covariance Estimation Three Ways” SIAM Conference on Uncertainty Quantification. Atlanta, GA, Apr 14, 2022.
- 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.
- 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.
- Han, J. and Lee, Y. “Inhomogeneous Regularization with Limited and Indirect Data” SIAM Conference on Imaging Sciences. virtual, Mar 21, 2022.
- Marzouk, Y., University of Nottingham, School of Mathematical Sciences, Statistics and Probability seminar. Nottingham, UK (virtual). March 2022.
- Glaubitz, J., Gelb, A., and Song, G. “Sparse Bayesian image reconstruction: Towards a unified approach” SIAM Conference on Imaging Science. virtual, Mar 24, 2022.
- Parno, M., Zhou, B., and Ronan, J. “A Practical Tour of Computational Optimal Transport” DoMSS seminar. Arizona State University, Feb 21, 2022.
- Marzouk, Y., Texas A&M University, Department of Industrial and Systems Engineering. College Station, Texas (virtual). February 2022.
- Lee, Y. “Hierarchical Learning to Solve Partial Differential Equations Using Physics-Informed Neural Networks” Applied and Computational Mathematics seminar. UC Riverside, Feb 2, 2022.
- Lee, Y. “Hierarchical Learning to Solve Partial Differential Equations Using Physics-Informed Neural Networks” Machine Learning+X seminar. Brown University, Jan 7, 2022.
- Glaubitz, J. and Gelb, A. “Sparse Bayesian learning for image reconstruction with uncertainty quantification” AFOSR 2022 Annual EM Portfolio Review. virtual, Jan 4, 2022.
- 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.
- Han, J. “Hierarchical Learning to Solve Partial Differential Equations Using Physics-Informed Neural Networks” National Institute of Mathematical Sciences Seminar. virtual, Dec 16, 2021.
- Lee, Y. “Finding a needle in sand beach” Applied Mathematics Seminar. Seoul National University, Dec 15, 2021.
- Han, J. “Hierarchical Learning to Solve Partial Differential Equations Using Physics-Informed Neural Networks” Applied Mathematics Seminar. Sungkyunkwan University, Dec 14, 2021.
- Lee, Y. “Finding a needle in sand beach” Applied Mathematics Seminar. Sungkyunkwan University, Dec 14, 2021.
- Lee, Y. “Finding a needle in sand beach” Applied Mathematics Seminar. Yonsei University, Dec 10, 2021.
- Han, J. “Hierarchical Learning to Solve Partial Differential Equations Using Physics-Informed Neural Networks” Applied Mathematics Seminar. Kyungpook National University, Dec 08, 2021.
- Lee, Y. “Finding a needle in sand beach” Applied Mathematics Seminar. Kyungpook National University, Dec 08, 2021.
- Lee, Y. “Finding a needle in sand beach” Mathematics Colloquium. UNIST, Dec 06, 2021.
- Han, J. “Hierarchical Learning to Solve Partial Differential Equations Using Physics-Informed Neural Networks” KSIAM Fall Meeting. Busan, South Korea, Dec 02, 2021.
- Marzouk, Y., Banff workshop, “Statistical Aspects of Nonlinear Inverse Problems.” Banff, Canada (virtual). November 2021.
- Parno, M. “When Models and Reality Collide: Model discrepancy in deterministic and Bayesian inverse problems” Clarkson Mathematics Colloquium. Clarkson University, Oct 18, 2021.
- Marzouk, Y., George Washington University, Center for Mathematics and Artificial Intelligence (CMAI) Colloquium (virtual). October 2021.
- Marzouk, Y., Centre International de Rencontres Mathematiques (CIRM) workshop, “On future synergies for stochastic and learning algorithms.” Marseille, France (virtual). September 2021.
- Marzouk, Y., CERN, Seminar on Machine Learning for Simulation. Geneva, Switzerland (virtual). September 2021.
- Marzouk, Y., University of Potsdam. SFB 1294 kickoff meeting (keynote). Potsdam, Germany (virtual). September 2021.
- Lee, Y. “Data Interpretation and Quantification for Inverse Problems” Image Analysis and Data Processing in Superresolution Microscopy. Prague, Czech, Sep 01, 2021.
- Glaubitz, J., and Gelb, A. “High order edge sensors with l1 regularization for enhanced discontinuous Galerkin methods” ICOSAHOM 2020. virtual, Jul 14, 2021.
- Marzouk, Y., RWTH Aachen University, Chair of Mathematics for Uncertainty Quantification, Seminar. Aachen, Germany (virtual). July 2021.
- Marzouk, Y., Bath-ICMS workshop on “Analytical and Geometric Approaches to Machine Learning.” Invited speaker. Bath, United Kingdon (virtual). July 2021.
- Marzouk, Y., Bernoulli-IMS 10th World Congress on Probability and Statistics. Invited session speaker. Seoul, Korea (virtual). July 2021.
- Marzouk, Y., SIAM Annual Meeting (AN21). Invited minisymposium (virtual). July 2021.
- Glaubitz, J., Öffner, P., and Gelb, A. “Least Squares Formulas - The Swiss Army Knife of Numerical Integration?” SIAM Annual Meeting. virtual, Jul 19, 2021.
- Parno, M., and Ronan, J. “Applications of Optimal Transport in Sea Ice Dynamics” SIAM Annual Meeting. virtual, Jul 20, 2021.
- Gelb, A.
*Empirical Bayesian Inference Using Joint Sparsity*, Keynote Speaker, International Conference on Computational Science (virtual), Krakow, Poland, June 17, 2021.

- 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.
- 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.
- Friderick, C., Lee, Y., Ren, K., Yang, Y., “New Ideas in Computational Inverse Problems”, Banff International Research Station, Oct 24-28, 2022.
- Lee, Y., Parno, M., “Sea Ice Modeling and Data Assimilation”, Minisymposium at SIAM Mathematics of Planet Earth, Jul 14-15, 2022.
- Parno, M. “An Introduction to Sampling with Measure Transport” Tutorial at SIAM Annual Meeting. Pittsburgh, PA, Jul 13, 2022.
- 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.
- Marzouk, Y., Co-organizer, Oberwolfach workshop on “Data assimilation: mathematical foundations and applications.” Oberwolfach, Germany. February 2022.
- Lee, Y., Choi, M., “Scientific Computing and Machine Learning”, Minisymposium at KSIAM Fall Meeting, Dec 3, 2021.
- Parno, M. “Data Science, Remote Sensing, and Uncertainty Quantification for Sea Ice” Minisymposium at SIAM Annual Meeting. virtual, Jul 20, 2021.
- Marzouk, Y., Co-chair of organizing committee, 021 SIAM Conference on Mathematical and Computational Issues in the Geosciences (GS21). June 2021.
- Marzouk, Y., Organizing committee, Department of Energy Workshop on Data Reduction for Science. February 2021.

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

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