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Physics Informed Neural Networks - BC Incorporation

Offered By: NPTEL-NOC IITM via YouTube

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Physics Informed Neural Networks Courses Machine Learning Courses Neural Networks Courses Differential Equations Courses Scientific Computing Courses Numerical Methods Courses Computational Physics Courses

Course Description

Overview

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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

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