MS344D Agentic AI and Physics-Informed Machine Learning for Next-Generation Design and Manufacturing IV
Main Organizer:
Prof.
Seunghwa Ryu
(
KAIST
, Republic of Korea
)
Scheduled presentations:
-
Keynote
Accelerating Ultrasonic Wavefield Simulations Using Physics-informed Neural Networks
-
Physics-Informed Neural Network-Based Discovery of Hyperelastic Constitutive Models from Extremely Scarce Data
-
Nonparametric Identification of Multimaterial Structure via Physics-Informed Neural Networks
-
Physics-Informed Deep Operator Network for Predicting Temperature Field of a Lithium-Ion Battery Under Variable Operating Conditions
-
An Autonomous Multiscale Simulation Framework for Ion Transport in High-Aspect-Ratio Plasma Etching
