Modelling of systems where the full system information is unknown is an oft encountered problem for various engineering and industrial applications, as it's either impossible to consider all the complex physics involved or simpler models are considered to keep within the limits of the available resources. Recent advances in greybox modelling like the deep hidden physics models address this space by combining data and physics. However, for most real-life applications, model generalizability is a key issue, as retraining a model for every small change in system inputs and parameters or modification in domain configuration can render the model economically unviable. In this work we present a novel enhancement to the idea of hidden physics models which can generalize for changes in system inputs, parameters and domains. We also show that this approach holds promise in system discovery as well and helps learn the hidden physics for the changed system inputs, parameters and domain configuration.

翻译：在系统完整信息未知的情况下进行建模是各类工程与工业应用中常见的问题，原因在于要么无法考虑所有涉及的复杂物理机制，要么为控制在可用资源范围内而采用简化模型。近期灰箱建模的进展，如深度隐式物理模型，通过融合数据与物理知识解决了这一问题。然而，对于大多数实际应用场景而言，模型泛化能力是关键问题——若系统输入、参数的微小变化或域配置的修改都需要重新训练模型，将导致模型在经济上不可行。本文提出了一种对隐式物理模型思想的新颖增强方案，使其能够适应系统输入、参数和域的变化而实现泛化。我们还证明，该方法在系统发现方面同样具有潜力，有助于学习变化后系统输入、参数和域配置下的隐藏物理规律。