Simulation-based engineering has been a major protagonist of the technology of the last century. However, models based on well established physics fail sometimes to describe the observed reality. They often exhibit noticeable differences between physics-based model predictions and measurements. This difference is due to several reasons: practical (uncertainty and variability of the parameters involved in the models) and epistemic (the models themselves are in many cases a crude approximation of a rich reality). On the other side, approaching the reality from experimental data represents a valuable approach because of its generality. However, this approach embraces many difficulties: model and experimental variability; the need of a large number of measurements to accurately represent rich solutions (extremely nonlinear or fluctuating), the associate cost and technical difficulties to perform them; and finally, the difficulty to explain and certify, both constituting key aspects in most engineering applications. This work overviews some of the most remarkable progress in the field in recent years.

中文翻译：

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用数据、机器学习和人工智能赋能工程：简短的介绍性回顾

基于仿真的工程一直是上个世纪技术的主要主角。然而，基于完善物理学的模型有时无法描述观察到的现实。它们通常在基于物理的模型预测和测量之间表现出明显的差异。这种差异有几个原因：实际（模型中涉及的参数的不确定性和可变性）和认知（模型本身在许多情况下是丰富现实的粗略近似）。另一方面，从实验数据中接近现实是一种有价值的方法，因为它具有普遍性。然而，这种方法包含许多困难：模型和实验的可变性；需要大量测量来准确表示丰富的解决方案（极度非线性或波动），执行它们的相关成本和技术困难；最后，解释和证明的难度，这两者都是大多数工程应用中的关键方面。这项工作概述了该领域近年来取得的一些最显着的进展。