Real-life problems are often characterized by high (geometric) complexity and uncertainty, both in the knowledge of the models and the data of the problem itself. At MOX, we develop methods aimed at reducing the computational cost associated with the numerical approximation of differential models, thereby mitigating their complexity. This allows not only for the real-time solution of physically-sound problems, but also for the control of the accuracy of the reduced models and for the study of how data uncertainty propagates into model outputs.
- Mesh adaptation
- Uncertainty quantification
- Projection-based model order reduction