Npj Comput. Mater.: 如何给碳复合材料算上一卦？

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Fig. 1 | A schematic illustrating the I-CVI process.

来自美国圣母大学航空航天与机械工程系的罗腾飞教授和王建勋教授领导的团队提出了一种用于等温化学气相浸渗（I-CVI）过程的物理集成神经微分（PiNDiff）模型。他们开发并验证了PiNDiff模型，通过全面的数值实验，展示了该模型在各种操作条件下的强大泛化能力，并显示了在实际制造场景中优化CVI过程的潜力。

Fig. 2 | Extracted foundational physics of the I-CVI process with neural operator approximations.

除了泛化能力外，他们还在PiNDiff模型中嵌入了不确定性量化（UQ）功能，利用贝叶斯模型平均（BMA）和蒙特卡罗积分来量化预测不确定性。这种方法提供了模型参数的概率分布，增强了模型的预测可靠性和稳健性，即使数据有限也能维持可信的不确定性范围。他们还能够展示出强大的外推能力，能够准确预测超出训练数据范围的情况，并保持一个可信的不确定性边界，覆盖了真实情况。这些特性显著提高了在实际场景中优化CVI过程的能力。

Fig. 3 | The overview of the learning architecture of the PiNDiff model for the I-CVI process.

作者的这项研究突出了PiNDiff框架作为一种工具的潜力，可以推动我们对CVI制造过程的理解、模拟和优化，特别是在面对数据稀缺和对底层物理过程描述不完整的情况下。

Fig. 4 | Prediction and inference results of the trained PiNDiff I-CVI model with quantified uncertainty.

该文近期发表于npj Computational Materials 10: 120 (2024)，英文标题与摘要如下，点击左下角“阅读原文”可以自由获取论文PDF。

Probabilistic physics-integrated neural differentiable modeling for isothermal chemical vapor infiltration process 

Deepak Akhare, Zeping Chen, Richard Gulotty, Tengfei Luo & Jian-Xun Wang 

Chemical vapor infiltration (CVI) is a widely adopted manufacturing technique used in producing carbon-carbon and carbon-silicon carbide composites. These materials are especially valued in the aerospace and automotive industries for their robust strength and lightweight characteristics. The densification process during CVI critically influences the final performance, quality, and consistency of these composite materials. Experimentally optimizing the CVI processes is challenging due to the long experimental time and large optimization space. To address these challenges, this work takes a modeling-centric approach. Due to the complexities and limited experimental data of the isothermal CVI densification process, we have developed a data-driven predictive model using the physics-integrated neural differentiable (PiNDiff) modeling framework. An uncertainty quantification feature has been embedded within the PiNDiff method, bolstering the model’s reliability and robustness. Through comprehensive numerical experiments involving both synthetic and real-world manufacturing data, the proposed method showcases its capability in modeling densification during the CVI process. This research highlights the potential of the PiNDiff framework as an instrumental tool for advancing our understanding, simulation, and optimization of the CVI manufacturing process, particularly when faced with sparse data and an incomplete description of the underlying physics.