Comparative evaluation lies at the heart of science, and determining the accuracy of a computational method is crucial for evaluating its potential as well as for guiding future efforts. However, metrics that are typically used have inherent shortcomings when faced with the under-resolved solutions of real-world simulation problems. We show how to leverage the human visual system in conjunction with crowd-sourced user studies to address the fundamental problems of widely used classical evaluation metrics. We demonstrate that such user studies driven by visual perception yield a very robust metric and consistent answers for complex phenomena without any requirements for proficiency regarding the physics at hand. This holds even for cases away from convergence where traditional metrics often end up with inconclusive results. More specifically, we evaluate results of different essentially non-oscillatory (ENO) schemes in different fluid flow settings. Our methodology represents a novel and practical approach for scientific evaluations that can give answers for previously unsolved problems.

中文翻译：

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指出不同

比较评估是科学的核心，确定计算方法的准确性对于评估其潜力以及指导未来的努力至关重要。然而，当面对现实世界模拟问题的未充分解决的解决方案时，通常使用的指标具有固有的缺点。我们展示了如何利用人类视觉系统与众包用户研究相结合来解决广泛使用的经典评估指标的基本问题。我们证明，由视觉感知驱动的此类用户研究为复杂现象产生了非常强大的度量和一致的答案，而无需对手头的物理学有任何熟练程度的要求。这甚至适用于远离收敛的情况，传统指标往往以不确定的结果告终。进一步来说，我们评估了不同流体流动设置中不同的基本非振荡 (ENO) 方案的结果。我们的方法代表了一种新颖而实用的科学评估方法，可以为以前未解决的问题提供答案。