Abstract Analytical and empirical solutions to engineering problems are usually preferred because of their convenience in applications. However, they are not always accessible in complex problems. A new class of solutions, based on machine learning (ML) models such as regression trees and neural networks (NNs), are proposed and their feasibility and value are demonstrated through the analysis of fracture toughness measurements. It is found that both solutions based on regression trees and NNs can provide accurate results for the specific problem, but NN-based solutions outperform regression-tree-based solutions in terms of their simplicity. This example demonstrates that ML solutions are a major improvement over analytical and empirical solutions in terms of both reliable functionality and rapid deployment. When analytical solutions are not available, the use of ML solutions can overcome the limitations of empirical solutions and substantially change the way that engineering problems are solved.

中文翻译：

---

断裂力学问题的机器学习方法

摘要 工程问题的分析和经验解决方案通常是首选，因为它们在应用中很方便。然而，在复杂的问题中，它们并不总是可用的。提出了一类基于机器学习 (ML) 模型（例如回归树和神经网络 (NN)）的新解决方案，并通过对断裂韧性测量值的分析证明了它们的可行性和价值。发现基于回归树和神经网络的解决方案都可以为特定问题提供准确的结果，但基于神经网络的解决方案在简单性方面优于基于回归树的解决方案。这个例子表明，就可靠的功能和快速部署而言，ML 解决方案是对分析和经验解决方案的重大改进。