This review article highlights state-of-the-art data-driven techniques to
discover, encode, surrogate, or emulate constitutive laws that describe the
path-independent and path-dependent response of solids. Our objective is to
provide an organized taxonomy to a large spectrum of methodologies developed in
the past decades and to discuss the benefits and drawbacks of the various
techniques for interpreting and forecasting mechanics behavior across different
scales. Distinguishing between machine-learning-based and model-free methods,
we further categorize approaches based on their interpretability and on their
learning process/type of required data, while discussing the key problems of
generalization and trustworthiness. We attempt to provide a road map of how
these can be reconciled in a data-availability-aware context. We also touch
upon relevant aspects such as data sampling techniques, design of experiments,
verification, and validation.

该综述论文重点介绍了最先进的数据驱动技术，用于发现、编码、替代或仿真描述固体无路径依赖响应的本构法则。我们旨在提供一个有组织的分类体系，介绍过去几十年中开发的各种方法的优点和缺点，并讨论在不同尺度上解释和预测力学行为的不同技术。我们将方法分为基于机器学习和无模型方法，并根据解释能力和所需数据学习过程 / 类型进一步分类，同时讨论了一般化和可信性的关键问题。我们试图提供一个数据可用性感知背景下如何解决这些问题的路线图，并涉及数据采样技术、实验设计、验证和验证等相关方面。