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Graph-based Reinforcement Learning for Active Learning in Real Time: An Application in Modeling River Networks.

, , , , , , and . SDM, page 621-629. SIAM, (2021)

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Heterogeneous Stream-reservoir Graph Networks with Data Assimilation., , , , , , , and . ICDM, page 1024-1029. IEEE, (2021)Partial Differential Equation Driven Dynamic Graph Networks for Predicting Stream Water Temperature., , , , , , and . ICDM, page 11-20. IEEE, (2021)Physics-Guided Recurrent Graph Model for Predicting Flow and Temperature in River Networks., , , , , , , , , and 1 other author(s). SDM, page 612-620. SIAM, (2021)Physics-Guided Machine Learning from Simulation Data: An Application in Modeling Lake and River Systems., , , , , , , , and . ICDM, page 270-279. IEEE, (2021)Graph-based Reinforcement Learning for Active Learning in Real Time: An Application in Modeling River Networks., , , , , , and . SDM, page 621-629. SIAM, (2021)Graph-based Reinforcement Learning for Active Learning in Real Time: An Application in Modeling River Networks., , , , , , and . CoRR, (2020)Physics-Guided Recurrent Graph Networks for Predicting Flow and Temperature in River Networks., , , , , , , , , and 1 other author(s). CoRR, (2020)Modeling Reservoir Release Using Pseudo-Prospective Learning and Physical Simulations to Predict Water Temperature., , , , , , and . SDM, page 91-99. SIAM, (2022)Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning., , , , , and . CoRR, (2020)Physics-guided machine learning from simulated data with different physical parameters., , , , , , , , , and . Knowl. Inf. Syst., 65 (8): 3223-3250 (2023)