Multi-physics simulation and machine learning can extend engineering design beyond geometry towards earlier prediction of product performance. This talk will explore how design choices, material behaviour, and manufacturing conditions interact to influence the properties of the finished product, and how digital models can help engineers understand these relationships before committing to repeated physical prototyping and testing. The broader workflow connects design, simulation, prediction, validation, and refinement, enabling more informed engineering decisions and more targeted physical validation.