Abhishek Sheshadri

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High-order Discontinuous Galerkin (DG) methods have shown a lot of promise in being able to provide high accuracy and efficiency as well as the flexibility to handle complex unstructured grids. However in order to solve compressible flow problems effectively, we need to be able to detect discontinuities/shocks in the flow and capture them effectively such(More)
The goal of this paper is to present a verification and validation study of HiFiLES: a high-order LES solver developed in the Aerospace Computing Laboratory (ACL) at Stanford University. HiFiLES has been built on top of SD++ (Castonguay et al.) and achieves high-order spatial discretizations with the Energy-Stable Flux Reconstruction (ESFR) scheme on(More)
The main objective of this project is to devise an algorithm that is able to predict the locations of multiple dark matter halos in a given image of galaxy distributions precisely and without directional bias. It is based on the active Kaggle competition Observing Dark Worlds, and uses the data, rules and accuracy measures prescribed by the competition.
The flux reconstruction (FR) approach to high-order methods has proved to be a promising alternative to traditional discontinuous Galerkin (DG) schemes since they facilitate the adoption of explicit time-stepping methods suitable for parallel architectures like GPUs. The FR approach provides a parameterized family of schemes through which various classical(More)
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