High temperature design requires accurate constitutive models to describe material inelastic deformation and failure behavior. Oftentimes, calibrating accurate models devolves into the problem of fitting the model parameters against experimental test data. Here, we present the pyopmat package, an open source framework for calibrating constitutive models against experiment data subjected to various loading conditions using machine learning techniques. The package calculates the exact gradient of the model response with respect to the parameters using a combination of automatic differentiation and the adjoint method. Given this exact gradient, we compare the performance of several gradient-based optimization techniques in fitting realistic constitutive models against data. We demonstrate the efficiency and accuracy of our package through example problems using both synthetic data, generated using known parameter sets, under monotonic and cyclic loading conditions and also with an example applying the techniques developed here to actual high temperature creep-fatigue test data.

高温设计需要准确的本构模型来描述材料的非弹性变形和失效行为。通常，校准准确的模型会转化为根据实验测试数据拟合模型参数的问题。在这里，我们介绍pyopmat包，一个开源框架，用于使用机器学习技术根据各种加载条件下的实验数据校准本构模型。该包使用自动微分和伴随方法的组合计算模型响应相对于参数的精确梯度。给定这个精确的梯度，我们比较了几种基于梯度的优化技术在根据数据拟合现实本构模型时的性能。我们通过使用已知参数集在单调和循环加载条件下生成的合成数据的示例问题以及将此处开发的技术应用于实际高温蠕变疲劳测试数据的示例来证明我们的包的效率和准确性。