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Bayesian DA Supported Publications
2019
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Gupta, A., P.J. Haley, D.N. Subramani, and P.F.J. Lermusiaux, 2019. Fish Modeling and Bayesian Learning for the Lakshadweep Islands. In: OCEANS '19 MTS/IEEE Seattle, 27-31 October 2019, doi: 10.23919/OCEANS40490.2019.8962892
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Doshi, M.M., C.S. Kulkarni, W.H. Ali, A. Gupta, P.F.J. Lermusiaux, P. Zhan, I. Hoteit, and O.M. Knio, 2019. Flowmaps and Coherent Sets for Characterizing Residence Times and Connectivity in Lagoons and Coral Reefs: The Case of the Red Sea. In: OCEANS '19 MTS/IEEE Seattle, 27-31 October 2019, doi:10.23919/OCEANS40490.2019.8962643
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Ali, W.H., M.S. Bhabra, P.F.J. Lermusiaux, A. March, J.R. Edwards, K. Rimpau, and P. Ryu, 2019. Stochastic Oceanographic-Acoustic Prediction and Bayesian Inversion for Wide Area Ocean Floor Mapping. In: OCEANS '19 MTS/IEEE Seattle, 27-31 October 2019, doi:10.23919/OCEANS40490.2019.8962870
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Ali, W.H., M.H. Mirhi, A. Gupta, C.S. Kulkarni, C. Foucart, M.M. Doshi, D.N. Subramani, C. Mirabito, P.J. Haley, Jr., and P.F.J. Lermusiaux, 2019. SeaVizKit: Interactive Maps for Ocean Visualization. In: OCEANS '19 MTS/IEEE Seattle, 27-31 October 2019, doi:10.23919/OCEANS40490.2019.8962794
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Kulkarni, C.S. and P.F.J. Lermusiaux, 2019. Advection without Compounding Errors through Flow Map Composition. Journal of Computational Physics, 398, 108859. doi:10.1016/j.jcp.2019.108859
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Feppon, F. and P.F.J. Lermusiaux, 2019. The Extrinsic Geometry of Dynamical Systems Tracking Nonlinear Matrix Projections. SIAM Journal on Matrix Analysis and Applications, 40(2), 814–844. doi: 10.1137/18M1192780
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Gil, Y., S.A. Pierce, H. Babaie, A. Banerjee, K. Borne, G. Bust, M. Cheatham, I. Ebert-Uphoff, C. Gomes, M. Hill, J. Horel, L. Hsu, J. Kinter, C. Knoblock, D. Krum, V. Kumar, P.F.J. Lermusiaux, Y. Liu, C. North, V. Pankratius, S. Peters, B. Plale, A. Pope, S. Ravela, J. Restrepo, A. Ridley, H. Samet, and S. Shekhar, 2019. Intelligent Systems for Geosciences: An Essential Research Agenda. Communications of the ACM, 62(1), 76–84. doi:10.1145/3192335
2018
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Deluca, S., B. Rocchio, C. Foucart, C. Mirabito, S. Zanforlin, P.J. Haley, and P.F.J. Lermusiaux, 2018. Scalable Coupled Ocean and Water Turbine Modeling for Assessing Ocean Energy Extraction. In: Oceans '18 MTS/IEEE Charleston, 22-25 October 2018. doi:10.1109/oceans.2018.8604646
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Dutt, A., D.N. Subramani, C.S. Kulkarni, and P.F.J. Lermusiaux, 2018. Clustering of Massive Ensemble of Vehicle Trajectories in Strong, Dynamic and Uncertain Ocean Flows. In: Oceans '18 MTS/IEEE Charleston, 22-25 October 2018. doi:10.1109/oceans.2018.8604634
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Kulkarni, C.S., P.J. Haley, Jr., P.F.J. Lermusiaux, A. Dutt, A. Gupta, C. Mirabito, D.N. Subramani, S. Jana, W.H. Ali, T. Peacock, C.M. Royo, A. Rzeznik, and R. Supekar, 2018. Real-Time Sediment Plume Modeling in the Southern California Bight. In: Oceans '18 MTS/IEEE Charleston, 22-25 October 2018. doi:10.1109/oceans.2018.8653642
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Feppon, F. and P.F.J. Lermusiaux, 2018. Dynamically Orthogonal Numerical Schemes for Efficient Stochastic Advection and Lagrangian Transport. SIAM Review, 60(3), 595–625. doi:10.1137/16m1109394
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Feppon, F. and P.F.J. Lermusiaux, 2018. A Geometric Approach to Dynamical Model-Order Reduction. SIAM Journal on Matrix Analysis and Applications, 39(1), 510–538. doi:10.1137/16m1095202
2017
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Lermusiaux, P.F.J., D.N. Subramani, J. Lin, C.S. Kulkarni, A. Gupta, A. Dutt, T. Lolla, P.J. Haley Jr., W.H. Ali, C. Mirabito, and S. Jana, 2017. A Future for Intelligent Autonomous Ocean Observing Systems. The Sea. Volume 17, The Science of Ocean Prediction, Part 2, J. Marine Res. 75(6), pp. 765–813. https://doi.org/10.1357/002224017823524035
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Lermusiaux, P.F.J., P.J. Haley Jr., S. Jana, A. Gupta, C.S. Kulkarni, C. Mirabito,
W.H. Ali, D.N. Subramani, A. Dutt, J. Lin, A. Y. Shcherbina, C. M. Lee, and A. Gangopadhyay, 2017. Optimal Planning and Sampling Predictions for Autonomous and Lagrangian Platforms and Sensors in the Northern Arabian Sea. Oceanography 30(2):172–185, https://doi.org/10.5670/oceanog.2017.242.
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Lolla, T. and P.F.J. Lermusiaux, 2017b. A Gaussian Mixture Model Smoother for Continuous Nonlinear Stochastic Dynamical Systems: Applications. Monthly Weather Review, 145, 2763-2790 DOI:10.1175/MWR-D-16-0065.1.
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Lolla, T. and P.F.J. Lermusiaux, 2017a. A Gaussian Mixture Model Smoother for Continuous Nonlinear Stochastic Dynamical Systems: Theory and Scheme. Monthly Weather Review, 145, 2743-2761, DOI:10.1175/MWR-D-16-0064.1
MIT Theses
- Kulkarni, C.S., 2021. Prediction, Analysis, and Learning of Advective Transport in Dynamic Fluid Flows. PhD Thesis, Massachusetts Institute of Technology, Mechanical Engineering, February 2021.
- Lin, J., 2020. Minimum-Correction Second-Moment Matching: Theory, Algorithms and Applications. SM thesis, Massachusetts Institute of Technology, Department of Mechanical Engineering, Cambridge, MA, February 2020.
- Subramani, D., 2018. Probabilistic Regional Ocean Predictions: Stochastic Fields and Optimal Planning. Ph.D. Thesis, Massachusetts Institute of Technology, Department of Mechanical Engineering and Center for Computational Engineering, February 2018.
- Dutt, A., 2018. High order stochastic transport and Lagrangian data assimilation. SM thesis, Massachusetts Institute of Technology, Department of Mechanical Engineering, Cambridge, MA, February 2018.
- Feppon, F., 2017. Riemannian Geometry of Matrix Manifolds for Lagrangian Uncertainty Quantification of Stochastic Fluid Flows. SM thesis, Massachusetts Institute of Technology, Center for Computational Engineering, February 2017.
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Lolla, T., 2016. Path Planning and Adaptive Sampling in the Coastal Ocean. Ph.D. Thesis, Massachusetts Institute of Technology, Department of Mechanical Engineering, February 2016.
