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ML-SCOPE Supported Publications
2023
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Gupta, A., and P.F.J. Lermusiaux, 2023. Generalized Neural Closure Models with Interpretability. Scientific Reports, sub-judice.
doi:10.48550/arXiv.2301.06198
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Gupta, A. and P.F.J. Lermusiaux, 2023. Bayesian Learning of Coupled Biogeochemical-Physical Models. Progress in Oceanography, sub-judice. https://arxiv.org/abs/2211.06714
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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, sub-judice. https://arxiv.org/abs/2209.12351
MIT THESES
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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.
