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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

, , и . Journal of Computational physics, (2019)

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Kenneth E M George

George Mertzios

Combinatorial optimization and recognition of graph classes with applications to related models. TU Aachen, (2009)

Ursula E E Köhler

George Marinescu

George Biros

 

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Exact PDF equations and closure approximations for advective-reactive transport., , , и . J. Comput. Phys., (2013)Inferring solutions of differential equations using noisy multi-fidelity data., , и . CoRR, (2016)Electronic poster: visualizing multiscale simulation data., , , и . SC Companion, стр. 101-102. ACM, (2011)A New Class of Semi-Implicit Methods with Linear Complexity for Nonlinear Fractional Differential Equations., , , и . SIAM J. Sci. Comput., 40 (5): A2986-A3011 (2018)Discontinuous Spectral Element Methods for Time- and Space-Fractional Advection Equations., и . SIAM J. Sci. Comput., (2014)Inflow/Outflow Boundary Conditions for Particle-Based Blood Flow Simulations: Application to Arterial Bifurcations and Trees., , , , и . PLoS Comput. Biol., (2015)Tightly Coupled Atomistic-Continuum Simulations of Brain Blood Flow on Petaflop Supercomputers., , , , , и . Comput. Sci. Eng., 14 (6): 58-67 (2012)Stochastic Computational Fluid Mechanics., , , и . Comput. Sci. Eng., 9 (2): 21-29 (2007)Dual-level parallelism for deterministic and stochastic CFD problems., и . SC, стр. 42:1-42:17. IEEE Computer Society, (2002)A new computational paradigm in multiscale simulations: application to brain blood flow., , , , , , и . SC, стр. 5:1-5:5. ACM, (2011)