[Note: Minisymposium #315 merged with this minisymposium]
Danial Faghihi, University of Buffalo
Jian-xun Wang, University of Notre Dame
Kathryn Maupin, Sandia National Laboratories
Alireza Tabarraei, University of North Carolina at Charlotte
Hao Sun, Northeastern University
- Bayesian validation and selection of multi-scale/multi-physics models
- UQ analyses of high-fidelity discrete (molecular dynamics, agent-based) models
- Physics-informed machine/deep learning
- Data-driven discovery of physical laws
- The interface of UQ and AI
- Design, control, and decision making under uncertainty
- Integrated multi-scale modeling and image analyses
- Computational imaging
- Operator inference for model reduction and surrogate modeling
- Learning from high-dimensional and uncertain data
- Multi-level, multi-fidelity, and dimension reduction methods
- Learning the structure of the high-fidelity physics-based model from data
- UQ methods for stochastic, time-dependent models with high-dimensional parameter space
- Scalable, adaptive, and efficient UQ algorithms
- Extensible software framework for large-scale inference and UQ