Uncertainty Quantification in Biomedical Applications
Sprache des Titels:
Englisch
Original Kurzfassung:
Uncertainty Quantification is a broad term that encompasses several different techniques, from parameter estimation to uncertainty characterization and model calibration, able to predict the stochastic variations of a quantity of interest under variable conditions. It is well recognized that quantifying uncertainty is essential for computational predictions to have any real value. Such indication becomes of paramount importance in the case of biomedical modeling. In fact, despite major recent advancements, the application of patient-specific modeling in clinical practice still faces a critical barrier with respect to the variability in the simulation output, arising from different levels of uncertainty inside a model built from data. The incorrect assumption of perfect knowledge of the subject?s characteristics can entail questionable choices for instance in surgical planning, potentially leading to treatment failure. In general, primary sources of uncertainties may result from input variability (aleatory/irreducible uncertainty), such as anatomical definition, tissue characteristics (like elasticity or excitability) and unknown boundary conditions, or from a lack of knowledge (epistemic/reducible uncertainty), like modeling assumptions or the influence of yet unknown physical phenomena. Additional uncertainty can also arise at the simulation stage. The school is part of the RICAM Special Semester on Mathematical methods in Medicine, and will offer complementary training on UQ techniques with applications in state-of-the-art problems of biomedical research, from medical imaging, passing through experimental test cases, and addressing the computational modeling of bones, cornea, heart and gut. Lecturers will be renowned expert scientists from multidisciplinary fields. The school is intended for Master/PhD students and early career researchers, and aims at creating optimal conditions for future synergic and multidisciplinary cooperation.