This talk will begin with a brief snapshot of two case studies of the use of SciML in an industrial context, followed by a vision for computational decision-support and digital twins. Some recent developments in SciML will be reviewed, and an attempt will be made at providing a structured perspective on the current, somewhat chaotic landscape of modeling approaches. Following this, I will discuss various demands placed on models in scalable industrial applications, and opportunities for integration of structure within machine learning models will be highlighted. Particularly, I will touch upon two aspects: a) Introduction of physics constraints and adaptivity in reduced order models to greatly enhance predictive accuracy in complex problems, and b) Use of conditional parameterization to enforce mathematical structure and to sensitize models to secondary inputs.