Materials modeling has always been a challenging issue. Many challenges arise in such modelings, such as nonlinear material behavior, complex physics, large deformation, rich behavior in the thickness direction, coupled Multiphysics phenomenon... Moreover, many materials exhibit rich behavior in the thickness direction, hindering the use of classical simplifications, and imposing the need for an extremely refined mesh when using classical simulation techniques. Model reduction techniques appeared as a suitable solution to alleviate computational time. Many applications and material forming processes benefit from the advantages offered by model reduction techniques including solid deformation, heat transfer, fluid flow… Moreover, the recent development in data-driven modeling opened novel possibilities in materials modeling. In fact, a correction or an update of the simulation using data modeling has led to forming the so-called digital twins, improving the simulation with data-driven modeling. Data-driven modeling of materials where current models are inaccurate became also possible through the use of machine learning algorithms.
The goal of this work is to address the efficient building of digital twins in the framework of materials manufacturing processes and materials modeling. Recent advances in digital twin technologies use experimental results to correct the simulation, but also to include their variability in the running simulation when a ground truth can’t be defined experimentally. The goal of this special issue is to address the recent developments in model reduction techniques, data-driven modeling, and digital twins’ technologies along with their applications in materials modeling and materials forming processes.
We welcome Original Research, Review, Mini-Review, and Perspective articles. Areas to be covered in this Research Topic may include, but are not limited to:
• Digital twins technologies
• Data-driven materials modeling
• Model reduction applications in materials forming
• Digital twins of materials and processes
• Machine learning of materials models
Topic Editor Elias Cueto received financial support from ESI Group; Topic Editor Anaïs Barasinski received financial support from Arkema. The other Topic Editors declare no competing interests with regard to the Research Topic subject.
Materials modeling has always been a challenging issue. Many challenges arise in such modelings, such as nonlinear material behavior, complex physics, large deformation, rich behavior in the thickness direction, coupled Multiphysics phenomenon... Moreover, many materials exhibit rich behavior in the thickness direction, hindering the use of classical simplifications, and imposing the need for an extremely refined mesh when using classical simulation techniques. Model reduction techniques appeared as a suitable solution to alleviate computational time. Many applications and material forming processes benefit from the advantages offered by model reduction techniques including solid deformation, heat transfer, fluid flow… Moreover, the recent development in data-driven modeling opened novel possibilities in materials modeling. In fact, a correction or an update of the simulation using data modeling has led to forming the so-called digital twins, improving the simulation with data-driven modeling. Data-driven modeling of materials where current models are inaccurate became also possible through the use of machine learning algorithms.
The goal of this work is to address the efficient building of digital twins in the framework of materials manufacturing processes and materials modeling. Recent advances in digital twin technologies use experimental results to correct the simulation, but also to include their variability in the running simulation when a ground truth can’t be defined experimentally. The goal of this special issue is to address the recent developments in model reduction techniques, data-driven modeling, and digital twins’ technologies along with their applications in materials modeling and materials forming processes.
We welcome Original Research, Review, Mini-Review, and Perspective articles. Areas to be covered in this Research Topic may include, but are not limited to:
• Digital twins technologies
• Data-driven materials modeling
• Model reduction applications in materials forming
• Digital twins of materials and processes
• Machine learning of materials models
Topic Editor Elias Cueto received financial support from ESI Group; Topic Editor Anaïs Barasinski received financial support from Arkema. The other Topic Editors declare no competing interests with regard to the Research Topic subject.
