2,871 research outputs found

    The transition prediction toolkit: LST, SIT, PSE, DNS, and LES

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    The e(sup N) method for predicting transition onset is an amplitude ratio criterion that is on the verge of full maturation for three-dimensional, compressible, real gas flows. Many of the components for a more sophisticated, absolute amplitude criterion are now emerging: receptivity theory, secondary instability theory, parabolized stability equations approaches, direct numerical simulation and large-eddy simulation. This paper will provide a description of each of these new theoretical tools and provide indications of their current status

    Zero-Cost Proxies Meet Differentiable Architecture Search

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    Differentiable neural architecture search (NAS) has attracted significant attention in recent years due to its ability to quickly discover promising architectures of deep neural networks even in very large search spaces. Despite its success, DARTS lacks robustness in certain cases, e.g. it may degenerate to trivial architectures with excessive parametric-free operations such as skip connection or random noise, leading to inferior performance. In particular, operation selection based on the magnitude of architectural parameters was recently proven to be fundamentally wrong showcasing the need to rethink this aspect. On the other hand, zero-cost proxies have been recently studied in the context of sample-based NAS showing promising results -- speeding up the search process drastically in some cases but also failing on some of the large search spaces typical for differentiable NAS. In this work we propose a novel operation selection paradigm in the context of differentiable NAS which utilises zero-cost proxies. Our perturbation-based zero-cost operation selection (Zero-Cost-PT) improves searching time and, in many cases, accuracy compared to the best available differentiable architecture search, regardless of the search space size. Specifically, we are able to find comparable architectures to DARTS-PT on the DARTS CNN search space while being over 40x faster (total searching time 25 minutes on a single GPU)
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