Hydrogels are soft polymeric materials with promising applications in biomedical fields. Understanding their fracture behavior is crucial for optimizing device design and performance. However, predicting hydrogel fracture is challenging due to the complex interplay between material properties and environmental factors. In this study, a machine learning (ML) approach to predict hydrogel fracture behavior is presented. A multiscale hydrogel fracture model is developed to generate simulation data, which is used to train a predictive neural network model. The ML model utilizes a hierarchical architecture of convolution long short-term memory units to capture spatial and temporal dependencies in the data. Model predictions are found to closely match simulation results with high accuracy, demonstrating the ability to learn complex fracture processes. Comparison of crack lengths shows the model can generalize across different material parameters. This work highlights the potential of ML for advancing the understanding of hydrogel fracture and soft matter failure. The presented approach provides an efficient framework for predicting fracture in complex materials and systems.

中文翻译：

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使用机器学习和非均匀多尺度网络预测水凝胶的断裂

水凝胶是一种软质高分子材料，在生物医学领域具有广阔的应用前景。了解它们的断裂行为对于优化设备设计和性能至关重要。然而，由于材料特性和环境因素之间复杂的相互作用，预测水凝胶断裂具有挑战性。在这项研究中，提出了一种预测水凝胶断裂行为的机器学习（ML）方法。开发了多尺度水凝胶断裂模型来生成模拟数据，用于训练预测神经网络模型。ML 模型利用卷积长短期记忆单元的分层架构来捕获数据中的空间和时间依赖性。研究发现模型预测与模拟结果高度匹配，证明了学习复杂断裂过程的能力。裂纹长度的比较表明该模型可以推广不同的材料参数。这项工作凸显了机器学习在促进对水凝胶断裂和软物质失效的理解方面的潜力。所提出的方法为预测复杂材料和系统的断裂提供了一个有效的框架。