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ML-SCOPE Supported Publications
2023
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Rajagopal, E., A.N.S. Babu, T. Ryu, P.J. Haley, Jr., C. Mirabito, and P.F.J. Lermusiaux, 2023. Evaluation of Deep Neural Operator Models toward Ocean Forecasting. In: OCEANS '23 IEEE/MTS Gulf Coast, 25–28 September 2023, in press. doi:10.48550/arXiv.2308.11814
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Gupta, A., and P.F.J. Lermusiaux, 2023. Generalized Neural Closure Models with Interpretability. Scientific Reports 13, 10364. doi:10.1038/s41598-023-35319-w
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Gupta, A. and P.F.J. Lermusiaux, 2023. Bayesian Learning of Coupled Biogeochemical-Physical Models. Progress in Oceanography 216, 103050. doi:10.1016/j.pocean.2023.103050
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Foucart, C., A. Charous, and P.F.J. Lermusiaux, 2023. Deep Reinforcement Learning for Adaptive Mesh Refinement. Journal of Computational Physics 491, 112381. doi:10.1016/j.jcp.2023.112381
MIT THESES
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Suresh Babu, A.N., 2023. Stochastic Sea Ice Modeling with the Dynamically Orthogonal Equations. SM Thesis, Massachusetts Institute of Technology, Mechanical Engineering, September 2023.
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Foucart, C., 2023. High-order Discontinuous Galerkin Methods and Deep Reinforcement Learning with Application to Multiscale Ocean Modeling. Ph.D. Thesis, Massachusetts Institute of Technology, Department of Mechanical Engineering and Center for Computational Science and Engineering, September 2023.
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Jalan, A., 2023. Neural Closure Models for Chaotic Dynamical Systems. SM Thesis, Massachusetts Institute of Technology, Mechanical Engineering, February 2023.
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Gupta, A., 2022. Scientific Machine Learning for Dynamical Systems: Theory and Applications to Fluid Flow and Ocean Ecosystem Modeling. Ph.D. Thesis, Massachusetts Institute of Technology, Department of Mechanical Engineering, September 2022.
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Kulkarni, C.S., 2021. Prediction, Analysis, and Learning of Advective Transport in Dynamic Fluid Flows. PhD Thesis, Massachusetts Institute of Technology, Department of Mechanical Engineering and Center for Computational Science and Engineering, February 2021.
