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Compound Parameterization for a Quality Control of Outliers and Larger Errors in Neural Network Emulations of Model Physics

机译:模型物理神经网络仿真中离群值和较大误差的质量控制的复合参数化

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Development of neural network (NN) emulations depends significantly on our ability to generate a representative training set. Because of high dimensionality of the input domain that is in the order of several hundreds or more, it is rather difficult to co
机译:神经网络(NN)仿真的开发在很大程度上取决于我们生成具有代表性的训练集的能力。由于输入域的高维数在数百或更多的数量级,因此很难协调

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