Physics-Informed Machine Learning (PIML) has gained momentum in the last 5 years with scientists and researchers aiming to utilize the benefits afforded by advances in machine learning, particularly in deep learning. With large scientific data sets with rich spatio-temporal data and high-performance computing providing large amounts of data to be inferred and interpreted, the task of PIML is to ensure that these predictions, categorizations, and inferences are enforced by, and conform to the limits imposed by physical laws. In this work a new approach to utilizing PIML is discussed that deals with the use of physics-based loss functions. While typical usage of physical equations in the loss function requires complex layers of derivatives and other functions to ensure that the known governing equation is satisfied, here we show that a similar level of enforcement can be found by implementing more simpler loss functions on specific kinds of output data. The generalizability that this approach affords is shown using examples of simple mechanical models that can be thought of as sufficiently simplified surrogate models for a wide class of problems.

翻译：近五年来,与旨在利用机械学习进步,特别是深层学习进步所带来的好处的科学家和研究人员建立了势头。随着拥有丰富的时空数据和高性能计算的大量科学数据集,提供了大量需要推断和解释的数据,PIML的任务是确保这些预测、分类和推理由物理法实施并符合物理法规定的限度。在这项工作中,讨论了利用PIML的新办法,该办法涉及物理损失功能的使用。虽然在损失函数中通常使用物理方程式需要复杂的衍生物层和其他功能,以确保已知的正方程得到满足,但我们在这里表明,通过对特定种类的产出数据实施更简单的损失功能,可以找到类似的执行水平。这种方法所允许的通用性是使用简单机械模型的例子,可以认为这些模型对于广泛的问题来说是足够简化的代孕模型。