MS344A Agentic AI and Physics-Informed Machine Learning for Next-Generation Design and Manufacturing I
Main Organizer:
Prof.
Seunghwa Ryu
(
KAIST
, Republic of Korea
)
Chaired by:
Prof. Seunghwa Ryu (KAIST , Republic of Korea) , Prof. YuanTong Gu (Queensland University of Technology , Australia)
Prof. Seunghwa Ryu (KAIST , Republic of Korea) , Prof. YuanTong Gu (Queensland University of Technology , Australia)
Scheduled presentations:
-
Keynote
A Variational-informed Neural Operator for Topology Optimisation
-
A Physics-Informed Machine Learning for Topological Optimisation of Patient-Specific Medical Plates
-
Physically Consistent Inverse Design of Nonlinear Microstructures via Physics-Informed Diffusion Model
-
Text-Guided Multiscale Topology Optimization for Mechanical Anisotropy Design with TPMS
-
LLM-Driven Multi-Agent Framework for Autonomous Topology Optimization
