PI: Luca Daniel, Department of Electrical Engineering & Computer Science, MIT
PI: Ivan Oseledets, Center for Computational and Data-Intensive Science and Engineering, Skoltech
The main goal of this project is to develop uncertainty quantification algorithms and to enable technology transfer toward production-quality software for different applications, including advanced manufacturing, material design and power systems. The core of our mathematical technology is based on stochastic partial differential equations and stochastic ordinary differential equation solvers based on stochastic testing, hierarchical uncertainty quantification and efficient tensor decomposition techniques. These methods are crucial for allowing the new generation of Electronic Design Automation tools to deal with manufacturing uncertainties in a single design cycle, therefore significantly reducing the amount of time required for the creation of a new design.