Characterization, modelling, and design of materials have been traditionally addressed as deterministic problems to investigate the properties and performance of materials. However, uncertainties arise from a variety of sources, including the imperfections in manufacturing/processing and the assumptions or lack of knowledge associated with numerical models. Several problems may occur due to the use of deterministic models since the uncertainty in material behaviour may cause deviations in expected material properties, which can alter the performance of structures, or even cause the failure of critical systems. Therefore, characterization, modelling, and design of materials require the consideration of the influence of the uncertainties on material behaviour and properties to improve the performance and reliability of structures and critical systems.
The aim of the Research Topic is to cover the advances in the development and application of uncertainty quantification (UQ) techniques for investigating the effects of uncertainties on physical material behaviour and designing materials under uncertainty. The recent advances in this area include the development of theoretical, numerical, data-driven, and machine learning (ML) reinforced UQ methods with applications in materials science and engineering problems in different length scales. These applications range from modelling and design of materials by considering the effects of material uncertainties to the investigation of the manufacturing (processing)-induced uncertainties or experimental uncertainties with different characterization and testing techniques.
We welcome papers focusing on UQ for different materials (including but not limited to metals, alloys, polymers, composites, ceramics) in different length scales (ranging from the atomistic to the macro-scale). The experimental and computational studies concentrating on the effects of manufacturing imperfections on material properties are also welcomed. Technical papers addressing the applications of UQ methods as well as review papers on UQ techniques for materials science problems are encouraged. The topics of interest include the following:
• Applications of new UQ concepts in materials science
• Design of materials under uncertainty
• Investigations for multi-scale propagation of the material uncertainty
• Material characterization methods to determine uncertainties in experimental data
• Data-driven and machine learning (ML) reinforced UQ for materials
• Quantification of manufacturing-related uncertainties in materials
• Effects of uncertainty on physical material behaviour (i.e., mechanical, electronic, optical, and thermal properties, fatigue/failure behaviour)
• Development of novel UQ methods to be used in materials science
Characterization, modelling, and design of materials have been traditionally addressed as deterministic problems to investigate the properties and performance of materials. However, uncertainties arise from a variety of sources, including the imperfections in manufacturing/processing and the assumptions or lack of knowledge associated with numerical models. Several problems may occur due to the use of deterministic models since the uncertainty in material behaviour may cause deviations in expected material properties, which can alter the performance of structures, or even cause the failure of critical systems. Therefore, characterization, modelling, and design of materials require the consideration of the influence of the uncertainties on material behaviour and properties to improve the performance and reliability of structures and critical systems.
The aim of the Research Topic is to cover the advances in the development and application of uncertainty quantification (UQ) techniques for investigating the effects of uncertainties on physical material behaviour and designing materials under uncertainty. The recent advances in this area include the development of theoretical, numerical, data-driven, and machine learning (ML) reinforced UQ methods with applications in materials science and engineering problems in different length scales. These applications range from modelling and design of materials by considering the effects of material uncertainties to the investigation of the manufacturing (processing)-induced uncertainties or experimental uncertainties with different characterization and testing techniques.
We welcome papers focusing on UQ for different materials (including but not limited to metals, alloys, polymers, composites, ceramics) in different length scales (ranging from the atomistic to the macro-scale). The experimental and computational studies concentrating on the effects of manufacturing imperfections on material properties are also welcomed. Technical papers addressing the applications of UQ methods as well as review papers on UQ techniques for materials science problems are encouraged. The topics of interest include the following:
• Applications of new UQ concepts in materials science
• Design of materials under uncertainty
• Investigations for multi-scale propagation of the material uncertainty
• Material characterization methods to determine uncertainties in experimental data
• Data-driven and machine learning (ML) reinforced UQ for materials
• Quantification of manufacturing-related uncertainties in materials
• Effects of uncertainty on physical material behaviour (i.e., mechanical, electronic, optical, and thermal properties, fatigue/failure behaviour)
• Development of novel UQ methods to be used in materials science
