Forcing-informed resolvent analysis extracts data-consistent forcing and response modes for self-sustained flows by estimating input-output subspaces from nonlinear forcing snapshots.
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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.
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Forcing-informed resolvent analysis: Identification of input-output relations in self-sustained flows
Forcing-informed resolvent analysis extracts data-consistent forcing and response modes for self-sustained flows by estimating input-output subspaces from nonlinear forcing snapshots.
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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.