Generative Adversarial Networks and Mixture Density Networks-Based Inverse Modeling for Microstructural Materials Design.
Generative Adversarial Networks and Mixture Density Networks-Based Inverse Modeling for Microstructural Materials Design.
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DOI：
10.1007/s40192-022-00285-0
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发表时间：
2022
影响因子：
3.3
通讯作者：
Agrawal, Ankit
中科院分区：
文献类型：
--
作者：
Mao, Yuwei;Yang, Zijiang;Jha, Dipendra;Paul, Arindam;Liao, Wei-keng;Choudhary, Alok;Agrawal, Ankit
在科学应用中有两种广泛的建模范式：正向和反向。正演建模是根据已知的原因来估计观测值，而逆演建模则试图根据观测值来推断原因。反问题在科学应用中通常更关键，也更困难，因为它们试图探索无法直接观察到的原因。反问题在物理学、医疗保健、材料科学等领域有着广泛的应用。探索材料性能与微观结构之间的关系是材料科学中的反问题之一。微结构发现反问题的求解具有挑战性，因为它通常需要学习一对多的非线性映射。给定目标性质，存在表现出目标性质的多个不同的微结构，并且它们的发现也需要大量的计算时间。此外，微观结构的发现变得更加困难，因为属性的维度（输入）远低于微观结构的维度（输出）。在这项工作中，我们提出了一个由生成对抗网络和混合密度网络组成的框架，用于材料中结构-性能联系的逆建模，即，微观结构发现对于给定的属性。结果表明，与基线方法相比，所提出的框架可以克服上述挑战，并以有效的方式发现多个有前途的解决方案。
There are two broad modeling paradigms in scientific applications: forward and inverse. While forward modeling estimates the observations based on known causes, inverse modeling attempts to infer the causes given the observations. Inverse problems are usually more critical as well as difficult in scientific applications as they seek to explore the causes that cannot be directly observed. Inverse problems are used extensively in various scientific fields, such as geophysics, health care and materials science. Exploring the relationships from properties to microstructures is one of the inverse problems in material science. It is challenging to solve the microstructure discovery inverse problem, because it usually needs to learn a one-to-many nonlinear mapping. Given a target property, there are multiple different microstructures that exhibit the target property, and their discovery also requires significant computing time. Further, microstructure discovery becomes even more difficult because the dimension of properties (input) is much lower than that of microstructures (output). In this work, we propose a framework consisting of generative adversarial networks and mixture density networks for inverse modeling of structure–property linkages in materials, i.e., microstructure discovery for a given property. The results demonstrate that compared to baseline methods, the proposed framework can overcome the above-mentioned challenges and discover multiple promising solutions in an efficient manner.
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影响因子：
4.6
作者：
Jha D;Gupta V;Ward L;Yang Z;Wolverton C;Foster I;Liao WK;Choudhary A;Agrawal A
通讯作者：
Agrawal A
影响因子：
2.6
作者：
Thornton, K.;Nola, Samanthule;Olson, G. B.
通讯作者：
Olson, G. B.
DOI：
10.1109/tim.2005.858132
发表时间：
2005-12-01
影响因子：
5.6
作者：
Hughes, D;Zoughi, R
通讯作者：
Zoughi, R
影响因子：
6.1
作者：
Agrawal, Ankit;Choudhary, Alok
通讯作者：
Choudhary, Alok
影响因子：
3.7
作者：
Meredig, B.;Agrawal, A.;Wolverton, C.
通讯作者：
Wolverton, C.