Physics perception very often faces the problem that only limited data or partial measurements on the scene are available. In this work, we propose a strategy to learn the full state of sloshing liquids from measurements of the free surface. Our approach is based on recurrent neural networks (RNN) that project the limited information available to a reduced-order manifold so as to not only reconstruct the unknown information, but also to be capable of performing fluid reasoning about future scenarios in real time. To obtain physically consistent predictions, we train deep neural networks on the reduced-order manifold that, through the employ of inductive biases, ensure the fulfillment of the principles of thermodynamics. RNNs learn from history the required hidden information to correlate the limited information with the latent space where the simulation occurs. Finally, a decoder returns data back to the high-dimensional manifold, so as to provide the user with insightful information in the form of augmented reality. This algorithm is connected to a computer vision system to test the performance of the proposed methodology with real information, resulting in a system capable of understanding and predicting future states of the observed fluid in real-time.
翻译:物理感知常常面临一个问题,即现场只有有限的数据或部分测量数据。在这项工作中,我们提出一个战略,从自由表面的测量中了解流失液体的全部状态。我们的方法基于经常性神经网络(RNN),将有限的信息投射到减序元体,以便不仅重建未知信息,而且能够实时对未来情景进行流畅推理。为了获得物理上一致的预测,我们用减序元体对深神经网络进行培训,通过引入偏差,确保热力学原则的实现。从历史中学习所需的隐藏信息,将有限的信息与模拟发生的潜在空间联系起来。最后,解码器将数据反馈到高维体体体体,以便以增强的现实形式向用户提供有洞察力的信息。这一算法与计算机视觉系统相连,以便用真实信息测试拟议方法的性能,从而形成一个能够理解和预测实时观察到的液体未来状态的系统。