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Extended Physics-informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition based Deep Learning Framework for Nonlinear Partial Differential Equations., and . AAAI Spring Symposium: MLPS, volume 2964 of CEUR Workshop Proceedings, CEUR-WS.org, (2021)Time-dependent and outflow boundary conditions for Dissipative Particle Dynamics., , and . J. Comput. Phys., 230 (10): 3765-3779 (2011)Modeling electrokinetic flows by the smoothed profile method., , and . J. Comput. Phys., 229 (10): 3828-3847 (2010)Adaptive ANOVA decomposition of stochastic incompressible and compressible flows., , , and . J. Comput. Phys., 231 (4): 1587-1614 (2012)A new domain decomposition method with overlapping patches for ultrascale simulations: Application to biological flows., and . J. Comput. Phys., 229 (15): 5541-5563 (2010)Multi-fidelity Gaussian process regression for prediction of random fields., , , and . J. Comput. Phys., (2017)A robust bi-orthogonal/dynamically-orthogonal method using the covariance pseudo-inverse with application to stochastic flow problems., , , and . J. Comput. Phys., (2017)Hidden physics models: Machine learning of nonlinear partial differential equations., and . J. Comput. Phys., (2018)Uncertainty quantification in simulation science., and . J. Comput. Phys., 217 (1): 1-4 (2006)The multi-element probabilistic collocation method (ME-PCM): Error analysis and applications., , and . J. Comput. Phys., 227 (22): 9572-9595 (2008)