Optimal experimental design is a well studied field in applied science and engineering. Techniques for estimating such a design are commonly used within the framework of parameter estimation. Nonetheless, in recent years parameter estimation techniques are changing rapidly with the introduction of deep learning techniques to replace traditional estimation methods. This in turn requires the adaptation of optimal experimental design that is associated with these new techniques. In this paper we investigate a new experimental design methodology that uses deep learning. We show that the training of a network as a Likelihood Free Estimator can be used to significantly simplify the design process and circumvent the need for the computationally expensive bi-level optimization problem that is inherent in optimal experimental design for non-linear systems. Furthermore, deep design improves the quality of the recovery process for parameter estimation problems. As proof of concept we apply our methodology to two different systems of Ordinary Differential Equations.

翻译：最优实验设计是应用科学与工程领域中一个被广泛研究的课题。在参数估计框架内，估计此类设计的技巧已得到普遍应用。然而近年来，随着深度学习技术逐渐取代传统估计方法，参数估计技术正在发生快速变革。这反过来要求与这些新技术相关的最优实验设计方法也需相应调整。本文研究了一种利用深度学习的新型实验设计方法。我们证明，通过训练网络作为无似然估计器，可以显著简化设计流程，并规避非线性系统最优实验设计中固有的计算代价高昂的双层优化问题。此外，深度设计提升了参数估计问题中恢复过程的质量。作为概念验证，我们将该方法应用于两个不同的常微分方程系统。