Calibration of dynamic models to data is an important step in building building digital twins of HVAC equipment, thermal loads and control systems. Sometimes, when a model fails to calibrate to data, a possible cause is that the model has made too many sim- plifying assumptions and is missing physics. In this paper we propose a semi-automated approach, called Dyad Model Discovery, that can augment the physical equations of the model with symbolic expressions discovered from the data. We demonstrate this method on a digital twin of a transportation refrigeration unit to improve its predictive performance, trained using telemetry data. An engineer-in-the-loop workflow is proposed, which provides suggestions to the user which can then be accepted or rejected. This is the first AI-assisted engineering design workflow to our knowledge.

翻译：将动态模型校准至数据是构建暖通空调设备、热负荷及控制系统数字孪生体的关键步骤。当模型无法与数据校准时，一个可能原因是模型进行了过多简化假设而遗漏了物理机制。本文提出一种半自动化方法——Dyad模型发现法，该方法能够利用从数据中发现的符号表达式对模型的物理方程进行增强。我们以运输制冷单元的数字孪生体为例，使用遥测数据训练模型，验证了该方法在提升预测性能方面的有效性。文中提出了一种工程师参与循环的工作流程，可向用户提供建议方案供其采纳或否决。据我们所知，这是首个AI辅助的工程设计工作流程。