论文标题
MFNET：数据效率的全面学习多年替代的信息源网络
MFNets: Data efficient all-at-once learning of multifidelity surrogates as directed networks of information sources
论文作者
论文摘要
我们提出了一种从各种成本和准确性的信息来源组成的构造的方法。多重替代替代物将信息源之间的连接编码为有向的无环图，并通过基于梯度的最小化最小二乘物镜的最小化训练。尽管绝大多数最先进的方法在信息源之间采用层次结构，但我们的方法可灵活地结构化信息源，这些信息源可能不承认严格的层次结构。该公式具有两个优点：（1）由于可以针对应用程序量身定制的简约多重网络引起的数据效率提高； （2）对培训数据没有任何限制 - 我们可以结合对信息源的嘈杂，非巢的评估。从合成到基于物理学的计算力学模拟的数值示例表明，我们方法中的错误可能是高度的命令级，尤其是在低数据级方面，而不是单性效率和层次多尺度方法。
We present an approach for constructing a surrogate from ensembles of information sources of varying cost and accuracy. The multifidelity surrogate encodes connections between information sources as a directed acyclic graph, and is trained via gradient-based minimization of a nonlinear least squares objective. While the vast majority of state-of-the-art assumes hierarchical connections between information sources, our approach works with flexibly structured information sources that may not admit a strict hierarchy. The formulation has two advantages: (1) increased data efficiency due to parsimonious multifidelity networks that can be tailored to the application; and (2) no constraints on the training data -- we can combine noisy, non-nested evaluations of the information sources. Numerical examples ranging from synthetic to physics-based computational mechanics simulations indicate the error in our approach can be orders-of-magnitude smaller, particularly in the low-data regime, than single-fidelity and hierarchical multifidelity approaches.
