||One goal of Integrated Computational Materials Engineering (ICME) is to combine material-scale characteristics with a fundamental basis for modeling mechanical performance of materials in engineering applications. By incorporating uncertainty quantification methods with validated material models across time- and length-scales, a tangible outcome is to alleviate exhaustive testing programs through model-based prediction of mechanical performance variability. ICME-based approaches are further motivated by applications requiring new performance and efficiency standards, where, for instance, thinned components invalidate current practices because of a more direct sensitivity to material-scale mechanisms. To realize the potential benefits of ICME-based design, current limitations in the demonstrated validity and computational tractability must be overcome. These limitations become increasingly evident once the holistic process-to-performance cycle is considered. This symposium celebrates verification, validation and uncertainty quantification techniques, which are necessary to build trust within models for incorporation into applications.
This multidisciplinary symposium will serve to provide illustration of recent advances in ICME-based research and its extension to engineering application. The motivating objective is to bring together researchers in uncertainty quantification, materials science, computational science, and data analytics methods to discuss current limitations and likely paths forward. Of particular interest are methods which serve to reduce the stochastic and material modeling space, such that uncertainty quantification methods can become computationally tractable. This symposium seeks presentations which illustrate:
- Multiscale material modeling for propagation of uncertainties
- Integrated multiphysics methods for process-to-performance modeling
- Reduced-order modeling methods for the stochastic and/or model space
- Verification and validation studies for engineering-scale application of ICME
- Emerging methods for knowledge-extraction from the data sets that underpin ICME