Neural networks are powerful surrogates for numerous forward processes. The inversion of such surrogates is extremely valuable in science and engineering. The most important property of a successful neural inverse method is the performance of its solutions when deployed in the real world, i.e., on the native forward process (and not only the learned surrogate). We propose Autoinverse, a highly automated approach for inverting neural network surrogates. Our main insight is to seek inverse solutions in the vicinity of reliable data which have been sampled form the forward process and used for training the surrogate model. Autoinverse finds such solutions by taking into account the predictive uncertainty of the surrogate and minimizing it during the inversion. Apart from high accuracy, Autoinverse enforces the feasibility of solutions, comes with embedded regularization, and is initialization free. We verify our proposed method through addressing a set of real-world problems in control, fabrication, and design.
翻译:神经网络是许多前方过程的强大代孕器。 此类代孕器的反转在科学和工程中极具价值。 成功的神经反向方法的最重要特性是当在现实世界中部署时其解决方案的性能, 也就是在本地前方过程( 不仅仅是学习过的代孕装置 ) 。 我们提出自动反向, 一种高度自动化的反神经网络代孕方法。 我们的主要见解是寻找在可靠数据周围的反向解决方案,这些数据已经抽样形成前方过程,并用于培训代孕模型。 自动反向方法通过考虑到代孕的预测不确定性并在反向过程中将其最小化来找到这样的解决方案。 除了高度精确外, 自动反向执行解决方案的可行性, 与嵌入式的正规化一起, 并且是自由初始化的。 我们通过解决一系列真实世界在控制、制造和设计上的问题来验证我们提出的方法 。