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    title = "Online Learning of Entrainment Closures in a Hybrid Machine Learning Parameterization",
    journal = "Journal of Advances in Modeling Earth System (JAMES)",
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    url = "https://authors.library.caltech.edu/records/2x0kp-pkz30",
    id = "record",
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    doi = "10.1029/2024ms004485",
    volume = "16"
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@article{https://resolver.caltech.edu/CaltechAUTHORS:20220926-576391900.2,
    title = "Ensemble-Based Experimental Design for Targeting Data Acquisition to Inform Climate Models",
    journal = "Journal of Advances in Modeling Earth Systems",
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    url = "https://resolver.caltech.edu/CaltechAUTHORS:20220926-576391900.2",
    id = "record",
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    doi = "10.1029/2022ms002997",
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@article{https://resolver.caltech.edu/CaltechAUTHORS:20220810-402975000,
    title = "Training physics‐based machine‐learning parameterizations with gradient‐free ensemble Kalman methods",
    journal = "Journal of Advances in Modeling Earth Systems",
    year = "2022",
    url = "https://resolver.caltech.edu/CaltechAUTHORS:20220810-402975000",
    id = "record",
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    doi = "10.1029/2022ms003105"
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@article{https://resolver.caltech.edu/CaltechAUTHORS:20220815-504980000,
    title = {An Efficient Bayesian Approach to Learning Droplet Collision Kernels: Proof of Concept Using "Cloudy," a New n-Moment Bulk Microphysics Scheme},
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    year = "2022",
    url = "https://resolver.caltech.edu/CaltechAUTHORS:20220815-504980000",
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    doi = "10.1029/2022ms002994",
    volume = "14"
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    title = "Epidemic management and control through risk-dependent individual contact interventions",
    journal = "PLoS Computational Biology",
    year = "2022",
    url = "https://resolver.caltech.edu/CaltechAUTHORS:20220325-220336231",
    id = "record",
    issn = "1553-734X",
    doi = "10.1371/journal.pcbi.1010171",
    volume = "18",
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    title = "Ensemble Inference Methods for Models With Noisy and Expensive Likelihoods",
    journal = "SIAM Journal on Applied Dynamical Systems",
    year = "2022",
    url = "https://resolver.caltech.edu/CaltechAUTHORS:20210412-121307581",
    id = "record",
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    doi = "10.1137/21M1410853",
    volume = "21"
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    title = "Training physics-based machine-learning parameterizations with gradient-free ensemble Kalman methods",
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    title = "Parameter Uncertainty Quantification in an Idealized GCM With a Seasonal Cycle",
    journal = "Journal of Advances in Modelling Earth Systems",
    year = "2022",
    url = "https://resolver.caltech.edu/CaltechAUTHORS:20210823-173258629",
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    doi = "10.1029/2021MS002735",
    volume = "14"
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    title = {An efficient Bayesian approach to learning droplet collision kernels: Proof of concept using "Cloudy", a new n-moment bulk microphysics scheme},
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    title = "Ensemble-Based Experimental Design for Targeted High-Resolution Simulations to Inform Climate Models",
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    url = "https://resolver.caltech.edu/CaltechAUTHORS:20220119-572479000",
    id = "record",
    doi = "10.1002/essoar.10510142.1"
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@article{https://resolver.caltech.edu/CaltechAUTHORS:20210113-143919927,
    title = "Calibration and Uncertainty Quantification of Convective Parameters in an Idealized GCM",
    journal = "Journal of Advances in Modelling Earth Systems",
    year = "2021",
    url = "https://resolver.caltech.edu/CaltechAUTHORS:20210113-143919927",
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    doi = "10.1029/2020MS002454",
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