The inverse analysis of indentation curves, aimed at extracting the stress-strain curve of a material, has been under intense development for decades, with progress relying mainly on the use of analytical expressions derived from small data sets. Here, we take a fresh, data-driven perspective to this classic problem, leveraging machine learning techniques to advance indentation technology. Using a neural network (NN), we efficiently assess uniqueness and identify materials that have indistinguishable indentation responses without the need for complex, domain knowledge-based algorithms. We then demonstrate that inclusion of the residual imprint information resolves the non-uniqueness problem. We show that the elasto-plastic properties of a material can be learned directly from indentation pile-up. Notably, an accurate stress-strain curve can be derived using solely the applied indentation load and pile-up information, thereby eliminating the need for depth-sensing. We also present a systematic analysis of the machine learning model, covering important aspects such as prediction performance, sensitivity, feature selection, and permutation importance, providing insight for model development and evaluation. This study introduces and provides the groundwork of a machine-learning-based profilometry-informed indentation inversion (PI³) technique. It showcases the potential of machine learning as a transformative alternative when analytical solutions are difficult or impossible to obtain.

压痕曲线的逆分析旨在提取材料的应力-应变曲线，几十年来一直在大力发展，其进展主要依赖于使用从小数据集导出的解析表达式。在这里，我们以全新的、数据驱动的视角来解决这个经典问题，利用机器学习技术来推进压痕技术。使用神经网络 (NN)，我们可以有效评估独特性并识别具有难以区分的压痕响应的材料，而无需复杂的基于领域知识的算法。然后，我们证明包含残留印记信息可以解决非唯一性问题。我们证明材料的弹塑性特性可以直接从压痕堆积中获知。值得注意的是，仅使用所施加的压痕载荷和堆积信息就可以得出准确的应力-应变曲线，从而消除了深度感测的需要。我们还对机器学习模型进行了系统分析，涵盖了预测性能、灵敏度、特征选择和排列重要性等重要方面，为模型开发和评估提供了见解。本研究介绍并提供了基于机器学习的轮廓测量信息的压痕反演 (PI ³ ) 技术的基础。当难以或不可能获得分析解决方案时，它展示了机器学习作为变革性替代方案的潜力。