The objective of multifidelity modeling is to achieve both accurate and efficient predictions by combining high- and low-fidelity models. A flexible approach considering additive, multiplicative, and input correction factors is proposed to improve the low-fidelity model using high-fidelity data. The correction factors are estimated using a Bayesian approach. Collection of high-fidelity data is optimized with two different objectives for comparison: by minimizing the error between synthetic data and the multifidelity prediction; and by maximizing the expected information gain, where the information gain is measured between the prior and posterior distributions. Once the multifidelity model is trained with optimal high-fidelity simulations, it is used to optimize the placement of sensors on a wind-tunnel test specimen, again by comparing the minimum expected error and the maximum expected information gain from the experiment. In this work, the prediction and measurement quantity of interest is aerodynamic pressure on rigid panel geometries in high-speed flow, and the multifidelity prediction model combines piston theory (low-fidelity) and Reynolds-averaged Navier–Stokes (high-fidelity) models.

多保真度建模的目的是通过组合高保真度模型和低保真度模型来实现准确而有效的预测。为了提高使用高保真度数据的低保真度模型，提出了一种考虑加，乘和输入校正因子的灵活方法。使用贝叶斯方法估计校正因子。优化高保真数据的收集具有两个不同的比较目标：通过最小化合成数据和多保真度预测之间的误差；通过最大化预期的信息增益，在先验分布和后验分布之间测量信息增益。一旦使用最佳的高保真度模拟训练了多保真度模型，就可以使用它来优化传感器在风洞测试样本上的放置，通过比较实验中的最小预期误差和最大预期信息增益来进行比较。在这项工作中，感兴趣的预测和测量量是高速流动中刚性面板几何形状上的气动压力，而多保真度预测模型则结合了活塞理论（低保真度）和雷诺平均Navier–Stokes（高保真度）模型。