Physics Informed Neural Networks - BC Incorporation
Offered By: NPTEL-NOC IITM via YouTube
Course Description
Overview
Explore the incorporation of boundary conditions in Physics Informed Neural Networks (PINNs) through this informative 18-minute video lecture. Gain insights into the advanced techniques used to enhance the accuracy and reliability of PINNs by effectively integrating boundary conditions into the neural network architecture. Learn how this approach improves the ability of PINNs to solve complex physical problems while maintaining consistency with known physical laws and constraints at the boundaries of the domain.
Syllabus
Physics Informed Neural Networks -- BC incorporation
Taught by
NPTEL-NOC IITM
Related Courses
Scientific ComputingUniversity of Washington via Coursera Differential Equations in Action
Udacity Initiation à la théorie des distributions
École Polytechnique via Coursera Everything is the Same: Modeling Engineered Systems
Northwestern University via Coursera Analyse numérique pour ingénieurs
École Polytechnique Fédérale de Lausanne via Coursera