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.

我们提出了一种从不同成本和准确性的信息源集合中构建代理的方法。多保真代理将信息源之间的连接编码为有向无环图，并通过非线性最小二乘目标的基于梯度的最小化进行训练。虽然绝大多数最先进的技术都假设信息源之间存在层次连接，但我们的方法适用于可能不允许严格层次结构的灵活结构化信息源。该公式有两个优点：（1）由于可以根据应用程序定制的简约多保真网络提高了数据效率；(2) 对训练数据没有限制——我们可以结合对信息源的嘈杂、非嵌套评估。