MS109A Combining Physics-Based and Data-Driven Approaches for Uncertainty Quantification I
MS Corresponding Organizer:
Dr.
Augustin Persoons
(
KU Leuven
, Belgium
)
Scheduled presentations:
-
Advancements in Hybrid Modeling for Battery Electrode Manufacturing Utilizing a Physics-inspired Data-driven Approach
-
A Practical Data Generation Framework for 4D Automotive MIMO Radar: Enabling Deep Learning and Radar Performance Analysis
-
A Point-Cloud Enhanced Transfer Learning Approach for Crashworthiness Analysis
-
Mapping Manufacturing Variability to Composite Pressure Vessels’ Geometry
-
Machine Learning Prediction of Random Property Fields From Microstructure Images
-
Model Uncertainty Quantification and Selection for Deep Learning-based Simulation of Hysteresis with Stiffness and Strength Degradations
