[KSNVE] 소음진동 AI와 미래모빌리티

강의목차

Part I: Basic principles and techniques

• PINN: principles and history

• PINN: techniques for applications

Part II: case study for applications

• Knoeledge integration for SOH estimation of LIBs

• Multiphysics-informed DeepONet for predicting thermal runaway of LIBs

• Multiphysics-informed neural network: Applications on an electric motor for virtual sensing and PHM

Part III: Lab

• PINN: Poission equation

• XPINN: Poission equation

• Learning rate annealing: Navier-stocks equation

• 실습예제 소스