Uncertainty quantification is critical to achieving validated predictive computations in a wide range of scientific and engineering applications. The field relies on a broad range of mathematics and statistics foundations, with associated algorithmic and computational development. The stochastic modeling and uncertainty quantification group at UC Santa Cruz aims aims at developing new theoretical and computational methods for uncertainty quantification and dimensional reduction in large-scale stochastic dynamical systems. Relevant research areas are
- Multi-fidelity stochastic modeling
- Mori-Zwanzig approach to dimensional reduction and uncertainty quantification
- Stochastic optimal control
- Hierarchical tensor methods for high-dimensional dimensional PDEs
Group Members