MS344G Agentic AI and Physics-Informed Machine Learning for Next-Generation Design and Manufacturing VII
Main Organizer:
Prof.
Seunghwa Ryu
(
KAIST
, Republic of Korea
)
Scheduled presentations:
-
Multi-agent framework for micromechanical modeling, simulation, and applicability analysis
-
Rapid Inverse Design of Wire Braided Architectures Using Physics-Informed Neural Operator
-
Bayesian Optimization of Lattice-Truss Structures with Ultrawide Phononic Bandgaps
-
AI-Based Shape Optimization of Dryer Pipe Geometry
-
A Physics-Informed Machine Learning Framework for Aerodynamic Performance-Driven Shape Optimization
-
Physics-Guided Hybrid FEM–ML Framework for Structural Dynamics Response Estimation with Sparse Sensors
