论文标题
自动信息：不确定性意识到神经网络的倒置
Autoinverse: Uncertainty Aware Inversion of Neural Networks
论文作者
论文摘要
神经网络是众多远程过程的强大代理。这种代理人的反转在科学和工程中非常有价值。成功的神经反向方法的最重要属性是在现实世界中部署在现实世界中的解决方案（即在本地远期过程（不仅是学识渊博的替代物）上）的性能。我们提出了自动化的AutoInverse，这是一种高度自动化的神经网络代理的方法。我们的主要见解是在可靠数据的附近寻求反向解决方案，这些解决方案已被对远期过程进行采样，并用于训练替代模型。自动信息通过考虑替代物的预测不确定性并在反转过程中最小化，从而找到了这种解决方案。除了高精度外，自动验证可行性的解决方案具有嵌入式正规化，并且不含初始化。我们通过解决控制，制造和设计中的一系列现实世界问题来验证我们的方法。
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.
