Digitalization and Modelling

This topic covers computational, data-centric, and digital engineering methods that support predictive design and risk mitigation for materials in security technologies. Submissions may include multiscale modeling, physics-based simulation, data-driven approaches, and hybrid strategies that combine mechanistic understanding with machine learning. Contributions on constitutive material modeling, especially for impact and high-strain-rate loading, are also welcome. Relevant work spans digital twins for components and systems, model-assisted materials development, and computational workflows for accelerated qualification under extreme loads such as impact, high temperature, chemical exposure, and irradiation. Topics relating to experimental characterisation and validation, parameter identification, uncertainty quantification and inverse engineering are likewise invited. Contributions that demonstrate validation against experiments, transparent assumptions, and reproducible data pipelines are particularly encouraged, especially where they enable scalable decision-making from material selection to system-level performance.