Accelerating phase-field predictions via recurrent neural networks learning the microstructure evolution in latent space

Hu, Chia-Ren; Martin, Shawn; Dingreville, Remi Philippe Michel

doi:10.1016/j.cma.2022.115128

Cited by 46 publications

(23 citation statements)

References 67 publications

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“…RNN is more effective in learning nonlinear characteristics of sequences because they share parameters, are remembered, and are Turing complete. RNN is first used to describe the relationship between a sequence's current output and its past information [ 25 , 26 ].…”

Section: Models and Evaluation Methodsmentioning

confidence: 99%

Evaluation of the Practical Effects of Environmental Measures in the Conservation of Architectural Heritage in Yan’an Based on Recurrent Neural Networks

Wang

2022

Journal of Environmental and Public Health

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Yan’an is one of the “two holy places” of the Chinese nation and the Chinese revolution and is one of the first cities of historical and cultural significance and an outstanding tourist city in China, as announced by the state council. The evaluation of the effectiveness of environmental conservation is one of the very important elements of the conservation of Yan’an’s architectural heritage. However, the existing evaluation methods cannot provide new solutions for decision-making, the meaning of the comprehensive evaluation function is unclear, the naming clarity is low, there is less quantitative data and more qualitative components, and the results are not easily convincing. This paper proposes a method for evaluating the practical effects of environmental class measures in the conservation of Yan’an’s architectural heritage based on recurrent neural networks. The recurrent neural network makes full use of the memory function in the network, considers the causal relationship of the actual effect, and efficiently evaluates the existing measures. In comparison with factor analysis and hierarchical analysis, this paper has greater applicability in evaluating the practical effects of environmental measures in the conservation of Yan’an’s architectural heritage and is basically consistent with the results of the theoretical analysis. It provides a scientific basis for the construction and implementation of environmental measures for the architectural heritage of Yan’an.

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Section: Models and Evaluation Methodsmentioning

confidence: 99%

Evaluation of the Practical Effects of Environmental Measures in the Conservation of Architectural Heritage in Yan’an Based on Recurrent Neural Networks

Wang

2022

Journal of Environmental and Public Health

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“…[33] Earlier studies have attempted to improve phase field predictions using recurrent neural networks (RNN) such as long short-term memory and the gated recurrent unit, predicting latent features for the phase field method. [21,34,35] These precedents have leveraged the sequential prediction capability of RNNs. Using sequential structures produced by phase field simulations, these works used batches of initial structures, formed using the phase field method, to predict features in future time steps of the simulation.…”

Section: Deep Learning Approachesmentioning

confidence: 99%

“…Numerous successful precedents demonstrated the benefits of integrating AI with phase field simulations. These included the utilization of machine learning to construct analytical expressions, [ 17 ] the representation of high‐dimensional free energy surfaces using deep neural networks, [ 18 ] the application of principal component analysis for the analysis of the outcome of phase field modeling, [ 19 ] the prediction of the time evolution of phase field modeling through neural networks, [ 20–22 ] the integration of neural networks into the design pipeline of electromigration parameters, [ 23 ] etc. These excellent results show the significant potential of employing AI algorithms to advance the study of phase field models.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Model to Predict Ice Crystal Growth

et al. 2023

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The demand for highly specific and complex materials has made the development of controllable manufacturing processes crucial. Among the numerous manufacturing methods, casting is important because it is economical and highly flexible regarding the geometry of manufactured parts. Since solidification is an important stage in the casting process that influences the properties of the final product, the development of a controllable solidification process using modeling methods is necessary to create superior structural properties. However, traditional modeling methods are computationally expensive and require sophisticated mathematical schemes. Therefore, a deep learning model is proposed to predict the morphology of the dendritic crystal growth solidification process, along with a reinforcement learning model to control the solidification process. By training the deep learning model with data generated using the phase field method, the solidification process can be successfully predicted. The crystal growth structures are designed to be altered by adjusting the degree of supercooling in the deep learning model while implementing reinforcement learning to control the dendritic arteries. This research opens new avenues for applying artificial intelligence to the optimization of casting processes, with the potential to utilize it in the processing of advanced materials and to improve the target properties of material design.

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“…There exist a multitude of other options for dimensionality reduction of the feature space, such as isomap or kernel PCA. However, the nonlinear embeddings employed in these techniques can introduce distortions into the latent space that negate the benefits of PCA identified above (Hu et al, 2022).…”

Section: Materials Structure Representation and Quantificationmentioning

confidence: 99%

Digital Twins for Materials

et al. 2022

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Digital twins are emerging as powerful tools for supporting innovation as well as optimizing the in-service performance of a broad range of complex physical machines, devices, and components. A digital twin is generally designed to provide accurate in-silico representation of the form (i.e., appearance) and the functional response of a specified (unique) physical twin. This paper offers a new perspective on how the emerging concept of digital twins could be applied to accelerate materials innovation efforts. Specifically, it is argued that the material itself can be considered as a highly complex multiscale physical system whose form (i.e., details of the material structure over a hierarchy of material length) and function (i.e., response to external stimuli typically characterized through suitably defined material properties) can be captured suitably in a digital twin. Accordingly, the digital twin can represent the evolution of structure, process, and performance of the material over time, with regard to both process history and in-service environment. This paper establishes the foundational concepts and frameworks needed to formulate and continuously update both the form and function of the digital twin of a selected material physical twin. The form of the proposed material digital twin can be captured effectively using the broadly applicable framework of n-point spatial correlations, while its function at the different length scales can be captured using homogenization and localization process-structure-property surrogate models calibrated to collections of available experimental and physics-based simulation data.

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Accelerating phase-field predictions via recurrent neural networks learning the microstructure evolution in latent space

Cited by 46 publications

References 67 publications

Evaluation of the Practical Effects of Environmental Measures in the Conservation of Architectural Heritage in Yan’an Based on Recurrent Neural Networks

Evaluation of the Practical Effects of Environmental Measures in the Conservation of Architectural Heritage in Yan’an Based on Recurrent Neural Networks

Deep Learning Model to Predict Ice Crystal Growth

Digital Twins for Materials

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