To characterize a physical system to behave as desired, either its underlying governing rules must be known a priori or the system itself be accurately measured. The complexity of full measurements of the system scales with its size. When exposed to real-world conditions, such as perturbations or time-varying settings, the system calibrated for a fixed working condition might require non-trivial re-calibration, a process that could be prohibitively expensive, inefficient and impractical for real-world use cases. In this work, we propose a learning procedure to obtain a desired target output from a physical system. We use Variational Auto-Encoders (VAE) to provide a generative model of the system function and use this model to obtain the required input of the system that produces the target output. We showcase the applicability of our method for two datasets in optical physics and neuroscience.

翻译：要确定一个物理系统按预期行事的特点,要么必须先验地知道其基本管理规则,要么系统本身必须准确测量系统规模的全面测量的复杂程度。当系统暴露于真实世界状况,如扰动或时间变化设置时,为固定工作条件校准的系统可能需要非三轨再校正,这个过程对于现实世界使用的案例来说可能过于昂贵、低效和不切实际。在这项工作中,我们建议采用学习程序从物理系统中获取理想的目标输出。我们使用变化式自动电算器(VAE)提供系统功能的基因化模型,并使用这一模型获取产生目标输出的系统所需的输入。我们展示了我们在光学物理学和神经科学中两种数据集的适用性。