MS362A Scientific Machine Learning to Enable Real-time Inference for Digital Twins I
Main Organizer:
Dr.
Tim Wildey
(
Sandia National Labs
, United States
)
Scheduled presentations:
-
A Neural Network-Based Surrogate Model for Intelligent Prediction and Control of Caisson Sinking Attitude
-
Enhancing Accuracy and Interpretability in Electrophysiological Surrogates via CNN-DeepONets
-
Gradient-Informed Neural Networks for Data-Efficient Surrogate Modeling Using Prior Beliefs: Application to Diesel Engine Emissions Estimation
-
A Comparative Study of Physics-Informed and Finite Element-Informed Neural Networks for Non-Uniform Material Identification in Beams with Limited Measurements
-
A Real-time Digital Twin Analysis Method for Shield Tunneling Based on Physical-data Dual-driven Approach and Its Application
-
Deep Reinforcement Learning for Energy-Efficient Control in Smart Buildings
