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Probabilistic Density Function Method for Stochastic ODEs of Power Systems with Uncertain Power Input., , , , , , , и . SIAM/ASA J. Uncertain. Quantification, 3 (1): 873-896 (2015)Learning Coarse-Grained Potentials for Binary Fluids., , и . J. Chem. Inf. Model., 60 (8): 3731-3745 (2020)Physics-informed Machine Learning Method for Forecasting and Uncertainty Quantification of Partially Observed and Unobserved States in Power Grids., и . HICSS, стр. 1-7. ScholarSpace, (2019)Exact PDF equations and closure approximations for advective-reactive transport., , , и . J. Comput. Phys., (2013)Modeling electrokinetic flows by consistent implicit incompressible smoothed particle hydrodynamics., , , , и . J. Comput. Phys., (2017)Conditional Karhunen-Loève expansion for uncertainty quantification and active learning in partial differential equation models., , и . J. Comput. Phys., (2020)Physics-constrained deep neural network method for estimating parameters in a redox flow battery., , и . CoRR, (2021)Probability and Cumulative Density Function Methods for the Stochastic Advection-Reaction Equation., и . SIAM/ASA J. Uncertain. Quantification, 6 (1): 180-212 (2018)Discrete Models of Fluids: Spatial Averaging, Closure, and Model Reduction., , и . SIAM J. Appl. Math., 74 (2): 477-515 (2014)Stochastically Forced Ensemble Dynamic Mode Decomposition for Forecasting and Analysis of Near-Periodic Systems., , , , и . IEEE Access, (2022)