Manifold-adapted anisotropic radial basis functions, shaped by clustering, yield a global explicit non-intrusive reduced vector field that recovers chaotic invariant measures competitively with intrusive and neural models.
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An a posteriori framework implemented in PyMHD estimates numerical dissipation in Alfvénic, dynamo, and MRI-driven MHD turbulence, showing it has distinct spectral and anisotropic properties from physical dissipation.
Exploiting repeated block structures converts SpMV to SpMM in CFD operators while an inline coarse-to-fine mesh strategy reduces time to statistically steady state, producing speed-ups up to over 50 percent on tested cases.
citing papers explorer
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Manifold-adapted radial basis functions for reduced-order modelling of chaotic flows
Manifold-adapted anisotropic radial basis functions, shaped by clustering, yield a global explicit non-intrusive reduced vector field that recovers chaotic invariant measures competitively with intrusive and neural models.
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Characterization of Numerical Dissipation in Simulations of Magnetohydrodynamic Turbulence
An a posteriori framework implemented in PyMHD estimates numerical dissipation in Alfvénic, dynamo, and MRI-driven MHD turbulence, showing it has distinct spectral and anisotropic properties from physical dissipation.
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Exploiting repeated matrix block structures for more efficient CFD on modern supercomputers
Exploiting repeated block structures converts SpMV to SpMM in CFD operators while an inline coarse-to-fine mesh strategy reduces time to statistically steady state, producing speed-ups up to over 50 percent on tested cases.