From reduced order models to PINNs and neural operators, the exponential growth of neural network techniques has shifted industry toward a new paradigm: scientific machine learning (SciML). However, industrial use cases are becoming increasingly complex while demanding faster, near real-time turnaround, necessitating the adaption of SciML to drastically more difficult problems. At Siemens Technology, we are researching the latest machine learning advances to develop solutions for our businesses and products. In this talk, we present an overview of interesting applications of SciML for Siemens, and our recent work on accelerating digital twins for a variety of use cases, including inverse heat exchanger control, quasi real-time prediction of airbag deployment, and neural design optimization in large design spaces. Through these examples, we also share some challenges in applying SciML to industrial applications.