论文标题
调整复杂仿真代码的迭代方法
Iterative Method for Tuning Complex Simulation Code
论文作者
论文摘要
调整复杂的仿真代码是指通过调整代码中实现的一组实验数据的代码计算协议的过程。此过程属于反问题或模型校准类别。对于这个问题，一些研究人员采用了基于高斯过程（GP）元模型的近似非线性最小二乘（ANLS）方法。 ANLS方法的潜在缺点是元模型仅构建一次，此后未更新。为了解决这一困难，我们在这项研究中提出了一种迭代算法。在提出的算法中，通过最大似然估计和ANLS方法对模拟代码和GP元模型的参数进行了重新估算和更新。该算法反复使用计算机和实验数据，直到收敛为止。一项使用玩具模型（包括不精确的计算机代码）的研究表明，所提出的算法的性能优于ANLS方法和基于条件样的方法。最后，说明了核融合模拟代码的应用。
Tuning a complex simulation code refers to the process of improving the agreement of a code calculation with respect to a set of experimental data by adjusting parameters implemented in the code. This process belongs to the class of inverse problems or model calibration. For this problem, the approximated nonlinear least squares (ANLS) method based on a Gaussian process (GP) metamodel has been employed by some researchers. A potential drawback of the ANLS method is that the metamodel is built only once and not updated thereafter. To address this difficulty, we propose an iterative algorithm in this study. In the proposed algorithm, the parameters of the simulation code and GP metamodel are alternatively re-estimated and updated by maximum likelihood estimation and the ANLS method. This algorithm uses both computer and experimental data repeatedly until convergence. A study using toy-models including inexact computer code with bias terms reveals that the proposed algorithm performs better than the ANLS method and the conditional-likelihood-based approach. Finally, an application to a nuclear fusion simulation code is illustrated.
