MS030B Scientific Machine Learning and Uncertainty Quantification for Robust Digital Twins in Science and Engineering II
Main Organizer:
Dr.
Dimitrios Loukrezis
(
Siemens AG
, Germany
)
Chaired by:
Prof. Dimitris Giovanis (Johns Hopkins University , United States)
Prof. Dimitris Giovanis (Johns Hopkins University , United States)
Scheduled presentations:
-
Probabilistic methods for learning compact dynamical representations of nonlinear systems
-
Data-driven Uncertainty Quantification on Manifolds for Cardiac Digital Twins
-
Uncertainty Quantification in Machine Learning for Glass Transition Temperature Prediction of Polymers
-
Student
Comparing Surrogate Models for Real-Time Dynamic Reactor Simulations
-
Student
Data-Driven Reduced Order Modeling Framework for Surrogate Modeling and Uncertainty Quantification in Electric Machine Design
-
Digital Twinning Tools for 3D Bioprinting of Functional Materials
