A Self-Attention Legendre Graph Convolution Network for Rotating Machinery Fault Diagnosis. [PDF]
Ma J, Huang J, Liu S, Luo J, Jing L.
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Computational Modelling of Cancer Nanomedicine: Integrating Hyperthermia Treatment Into a Multiphase Porous-Media Tumour Model. [PDF]
Wirthl B +3 more
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Three-dimensional optofluidic control using reconfigurable thermal barriers. [PDF]
Schmidt F +6 more
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Comparative Analysis of Open-Source Finite Element Method Solvers for Computational Fluid Dynamics Performance in a Carotid Artery Model. [PDF]
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Comparison of Transcranial Magnetic Stimulation Dosimetry between Structured and Unstructured Grids Using Different Solvers. [PDF]
Camera F, Merla C, De Santis V.
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A Vascularized Multilayer Chip Reveals Shear Stress-Induced Angiogenesis in Diverse Fluid Conditions. [PDF]
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Efficient numerical simulation of physical processes is constrained by our ability to solve the resulting linear systems, prompting substantial research into the development of multiscale iterative methods capable of solving these linear systems with an optimal amount of effort.
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On the Algebraic Multigrid Method
Journal of Computational Physics, 1996zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Robustness and Scalability of Algebraic Multigrid
SIAM Journal on Scientific Computing, 2000The performance of classical algebraic multigrid (AMG) on a variety of problems having regular structure is studied first to determine its robustness. Secondly, the performance of AMG on the same suite of problems but now with unstructured grids and/or irregular domains is considered. The algorithmic scalability of AMG is considered on several problems
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The efficient utilization of parallel computational capabilities of modern hardware architecture is a must in large scale industrial applications. In this paper we focus on the parallelization of algebraic multigrid (AMG) in general and identify the respective challenges imposed on any hierarchical iterative linear solver.
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