In the realm of computational material modeling, advanced numerical techniques such as multiscale modeling and machine learning have revolutionized the design and development of new engineering products. The integration of convolutional neural networks and other machine learning approaches with traditional finite element methods has shown promise in enhancing computational efficiency while maintaining high accuracy levels. Despite these advancements, challenges persist in modeling heterogeneous materials, particularly in achieving a balance between computational effort and accuracy. Recent studies have demonstrated the efficacy of machine learning models in predicting material properties like permeability and stress-strain responses, and in homogenizing complex material behaviors. However, there remains a need for further exploration into the integration of machine learning with physical models, ensuring thermodynamic consistency, and addressing microstructural heterogeneity's impact on macro-scale behavior.
This research topic aims to advance the field of computational material modeling by exploring the intersection of machine learning techniques with traditional numerical methods. The goal is to develop and validate models that can efficiently and accurately predict the behavior of complex materials under various conditions. Specific objectives include improving the computational efficiency of multiscale modeling, ensuring the thermodynamic consistency of constitutive models, and enhancing the predictive capabilities of machine learning approaches for material properties. By addressing these aims, the research seeks to contribute to the optimization of material design processes and the reduction of development costs.
To gather further insights in the realm of computational homogenization and machine learning integration, we welcome articles addressing, but not limited to, the following themes:
- Development and validation of convolutional neural networks for predicting material properties from microstructural images
- Integration of machine learning with physical constitutive models for accurate material behavior prediction
- Approaches to ensure thermodynamic consistency and fulfill constitutive conditions in hyper-elastic models
- Techniques for efficient multiscale modeling and homogenization of heterogeneous materials
- Data-driven surrogate models for concurrent multiscale simulations
- Machine learning-driven approaches for multiscale mechanics modeling
- Neural network-based methods for generalizing multiscale analysis and reducing computational effort
- Studies on the impact of microstructural heterogeneity on macro-scale material behavior
- Novel numerical schemes for the implementation of complex material models, such as Gurson's model
- Multi-scale procedures for the analysis of composite structures with discontinuities
- Uncertainty quantification in data-driven discovery of material properties
- Integration of topology optimization with machine learning for advanced materials design
Keywords:
computational material science, hybrid modeling approaches, machine learning integration, functional material design, data-driven design
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
In the realm of computational material modeling, advanced numerical techniques such as multiscale modeling and machine learning have revolutionized the design and development of new engineering products. The integration of convolutional neural networks and other machine learning approaches with traditional finite element methods has shown promise in enhancing computational efficiency while maintaining high accuracy levels. Despite these advancements, challenges persist in modeling heterogeneous materials, particularly in achieving a balance between computational effort and accuracy. Recent studies have demonstrated the efficacy of machine learning models in predicting material properties like permeability and stress-strain responses, and in homogenizing complex material behaviors. However, there remains a need for further exploration into the integration of machine learning with physical models, ensuring thermodynamic consistency, and addressing microstructural heterogeneity's impact on macro-scale behavior.
This research topic aims to advance the field of computational material modeling by exploring the intersection of machine learning techniques with traditional numerical methods. The goal is to develop and validate models that can efficiently and accurately predict the behavior of complex materials under various conditions. Specific objectives include improving the computational efficiency of multiscale modeling, ensuring the thermodynamic consistency of constitutive models, and enhancing the predictive capabilities of machine learning approaches for material properties. By addressing these aims, the research seeks to contribute to the optimization of material design processes and the reduction of development costs.
To gather further insights in the realm of computational homogenization and machine learning integration, we welcome articles addressing, but not limited to, the following themes:
- Development and validation of convolutional neural networks for predicting material properties from microstructural images
- Integration of machine learning with physical constitutive models for accurate material behavior prediction
- Approaches to ensure thermodynamic consistency and fulfill constitutive conditions in hyper-elastic models
- Techniques for efficient multiscale modeling and homogenization of heterogeneous materials
- Data-driven surrogate models for concurrent multiscale simulations
- Machine learning-driven approaches for multiscale mechanics modeling
- Neural network-based methods for generalizing multiscale analysis and reducing computational effort
- Studies on the impact of microstructural heterogeneity on macro-scale material behavior
- Novel numerical schemes for the implementation of complex material models, such as Gurson's model
- Multi-scale procedures for the analysis of composite structures with discontinuities
- Uncertainty quantification in data-driven discovery of material properties
- Integration of topology optimization with machine learning for advanced materials design
Keywords:
computational material science, hybrid modeling approaches, machine learning integration, functional material design, data-driven design
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.