Computational models have seen a wide adoption in industry over the past decade. This is particularly true for design and analysis of complex engineered systems such as those in automotive and aerospace applications. These days models are being put in use beyond the design phase and into the operation phase. This new paradigm is enabling workflows like digital twins and digital engineering and continuous engineering methods like DevOps. The advent of machine learning components in models brings with it its own challenges such as explainability and robustness. Given where we are today, where has there been progress in demonstrating practical impact of research results over the past decade and where do open challenges still remain? In this talk we will explore these questions from the perspective of an industry research scientist, and survey progress and outline open challenges.

Dr. Akshay Rajhans is a Principal Research Scientist in and a founding member of the Advanced Research & Technology Office at MathWorks where his work focuses on identifying and nurturing collaborative research and innovation at the interface between the MathWorks Development organization and academia, industry, and government research labs. His work and expertise includes technical computing for, and model-based design and analysis of intelligent cyber-physical systems (CPS).