Model Order Reduction
RESEARCH TOPIC 2
Streamlining mathematical models for real-time simulations.
As powerful as they may be, numerical computation algorithms can require significant time to simulate highly complex phenomena. However, in many cases, real-time simulation is required—for example, during surgical procedures. To turn this need into a concrete possibility, the scientific community relies on Model Order Reduction (MOR) techniques: mathematical tools that streamline models, making calculations much faster while preserving a good degree of accuracy.
Research Topic 2 (RT2) explores the application of MOR in the field of computational fluid dynamics, a crucial discipline for simulating industrial, physiological, and environmental phenomena. Since every simplification inevitably introduces approximations—and thus small margins of error—RT2 draws on the science of uncertainty quantification to estimate, manage, and reduce uncertainties in the results.
Key words
Model Order Reduction
A collection of techniques to streamline mathematical models, based on the principle of keeping only the most relevant parameters within them.
Computational fluid dynamics
The branch of physics that studies and simulates how fluids move and interact—with other objects or with each other—through numerical computation algorithms.
Uncertainty quantification
Mathematical discipline dedicated to studying and estimating uncertainties in simulations and mathematical models.
Coordination
Research Topic 2 is led by the International School for Advanced Studies (SISSA). The University of Padua (UniPD) and the National Institute of Oceanography and Experimental Geophysics (OGS) are also involved in RT2.