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Physics-Guided Deep Learning for Drag Force Prediction in Dense Fluid-Particulate Systems.

, , , , , , and . Big Data, 8 (5): 431-449 (2020)

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Performance Enhancement on Microprocessors with Hierarchical Memory Systems for Solving Large Sparse Linear Systems., and . Int. J. High Perform. Comput. Appl., 13 (1): 63-79 (1999)Physics-guided Design and Learning of Neural Networks for Predicting Drag Force on Particle Suspensions in Moving Fluids., , , , , , and . CoRR, (2019)VisBench: A Framework for Remote Data Visualization and Analysis., , and . International Conference on Computational Science (2), volume 2074 of Lecture Notes in Computer Science, page 718-727. Springer, (2001)A Parallel Adaptive Mesh Refinement Algorithm for Solving Nonlinear Dynamical Systems., and . Int. J. High Perform. Comput. Appl., 18 (2): 171-181 (2004)Efficient parallel CFD-DEM simulations using OpenMP., , and . J. Comput. Phys., (2014)Recycling Krylov subspaces for CFD applications and a new hybrid recycling solver., , , , and . J. Comput. Phys., (2015)PhyNet: Physics Guided Neural Networks for Particle Drag Force Prediction in Assembly., , , , , , and . SDM, page 559-567. SIAM, (2020)The conference was canceled because of the coronavirus pandemic, the reviewed papers are published in this volume..A new approach for conjugate heat transfer problems using immersed boundary method for curvilinear grid based solvers., , and . J. Comput. Phys., (2014)High-Order Finite Difference Formulations for the Incompressible Navier-Stokes Equations on the CM-5.. PPSC, page 155-160. SIAM, (1995)Recycling Krylov subspaces for CFD applications., , , , and . CoRR, (2015)