论文标题
通过贝叶斯优化生成模型的贝叶斯优化来搜索所需的功能响应：铁电中的磁滞回路工程
Guided search for desired functional responses via Bayesian optimization of generative model: Hysteresis loop shape engineering in ferroelectrics
论文作者
论文摘要
跨多个学科的预测建模的进步已经产生了能够高真实性的生成模型，以预测材料的宏观功能响应。相应地，感兴趣的是找到模型参数的反问题，该参数将产生所需的宏观响应，例如应力 - 应变曲线，铁电磁滞循环循环等。在这里，我们建议并实施一种基于高斯流程的方法，可以有效地采样对复杂的非元素模型的简化参数，以实现型号的非元素模型，从而产生均值功能。我们讨论了采集函数和采样函数的特定适应性，以使过程有效，并平衡多个可能的最小值参数空间的有效探索，并剥削以密度对目标行为进行了优化的目标区域进行采样。通过铁电材料中的滞后循环工程来说明这种方法，但可以适用于其他功能和生成模型。该代码是开源的，可在[github.com/ramav87/ferrosim]上找到。
Advances in predictive modeling across multiple disciplines have yielded generative models capable of high veracity in predicting macroscopic functional responses of materials. Correspondingly, of interest is the inverse problem of finding the model parameter that will yield desired macroscopic responses, such as stress-strain curves, ferroelectric hysteresis loops, etc. Here we suggest and implement a Gaussian Process based methods that allow to effectively sample the degenerate parameter space of a complex non-local model to output regions of parameter space which yield desired functionalities. We discuss the specific adaptation of the acquisition function and sampling function to make the process efficient and balance the efficient exploration of parameter space for multiple possible minima and exploitation to densely sample the regions of interest where target behaviors are optimized. This approach is illustrated via the hysteresis loop engineering in ferroelectric materials, but can be adapted to other functionalities and generative models. The code is open-sourced and available at [github.com/ramav87/Ferrosim].
