The unceasing growth in computational power and the development of new software tools and numerical algorithms is opening up exciting areas of research, discovery & translation in mechanics and biomedical engineering.
Academic life at the university level, with its intrinsic freedom in most settings, is full of choices. These choices include high risk research, turning teaching on its head, taking extreme positions on ideology, becoming a perpetual cynic, making life run on an autopilot, or becoming a silent rebel by doing almost nothing.
Predictive digital twins and the data-driven future of computational science
Director of the Oden Institute for Computational
Engineering and Sciences, Associate Vice President for
Research, and Professor of Aerospace Engineering and
Engineering Mechanics, University of Texas, Austin
A digital twin is an evolving virtual model that mirrors an individual physical asset throughout its lifecycle. Key to the digital twin concept is the ability to sense, collect, analyze, and learn from the asset's data.
The ability to manufacture micro-scale sensors and actuators has inspired the robotics community for
over 30 years. There have been huge success stories; MEMS inertial sensors have enabled an entire market
of low-cost, small UAVs. However, the promise of ant-scale robots has largely failed.
Architected materials (or mechanical metamaterials)
derive their macroscopic properties from a carefully
engineered small-scale structural architecture - rather
than from composition and microstructure as in