Jeffrey S. Garretson | Searcharxiv

arXiv2019

GAMERA: A three-dimensional finite-volume MHD solver for non-orthogonal curvilinear geometries

Efficient simulation of plasmas in various contexts often involves the use of meshes that conform to the intrinsic geometry of the system under consideration. We present here a description of a new magnetohydrodynamic code, Gamera (Grid Agnostic MHD for Extended Research Applications), designed to combine geometric flexibility with high-order spatial reconstruction and constrained transport to maintain the divergence-free magnetic field. Gamera carries on the legacy of its predecessor, the LFM (Lyon-Fedder-Mobarry), a research code whose use in space physics has spanned three decades. At the time of its initial development the LFM code had a number of novel features: eighth-order centered spatial differencing, the Partial Donor Cell Method limiter for shock capturing, a non-orthogonal staggered mesh with constrained transport, and conservative averaging-reconstruction for axis singularities. A capability to handle multiple ion species was also added later. Gamera preserves the core numerical philosophy of LFM while also incorporating numerous algorithmic and computational improvements. The upgrades in the numerical schemes include accurate grid metric calculations using high-order Gaussian quadrature techniques, high-order upwind reconstruction, non-clipping options for interface values, and improved treatment of axis singularities. The improvements in the code implementation include the use of data structures and memory access patterns conducive to aligned vector operations and the implementation of hybrid parallelism, using MPI and OMP. Gamera is designed to be a portable and easy-to-use code that implements multi-dimensional MHD simulations in arbitrary non-orthogonal curvilinear geometries on modern supercomputer architectures.

physics.comp-ph↗

arXiv2017

Neural Reconstruction Integrity: A metric for assessing the connectivity of reconstructed neural networks

Neuroscientists are actively pursuing high-precision maps, or graphs, consisting of networks of neurons and connecting synapses in mammalian and non-mammalian brains. Such graphs, when coupled with physiological and behavioral data, are likely to facilitate greater understanding of how circuits in these networks give rise to complex information processing capabilities. Given that the automated or semi-automated methods required to achieve the acquisition of these graphs are still evolving, we develop a metric for measuring the performance of such methods by comparing their output with those generated by human annotators ("ground truth" data). Whereas classic metrics for comparing annotated neural tissue reconstructions generally do so at the voxel level, the metric proposed here measures the "integrity" of neurons based on the degree to which a collection of synaptic terminals belonging to a single neuron of the reconstruction can be matched to those of a single neuron in the ground truth data. The metric is largely insensitive to small errors in segmentation and more directly measures accuracy of the generated brain graph. It is our hope that use of the metric will facilitate the broader community's efforts to improve upon existing methods for acquiring brain graphs. Herein we describe the metric in detail, provide demonstrative examples of the intuitive scores it generates, and apply it to a synthesized neural network with simulated reconstruction errors.

q-bio.QM↗