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Enhancing Scientific Computing Through Physics-Informed Neural Networks

Offered By: Alan Turing Institute via YouTube

Tags

Scientific Computing Courses Data Science Courses Machine Learning Courses Deep Learning Courses Numerical Methods Courses Partial Differential Equations Courses Computational Physics Courses Physics Informed Neural Networks Courses

Course Description

Overview

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Explore the potential of physics-informed deep learning (PIDL) in scientific computing through this comprehensive lecture. Delve into physics-informed neural networks (PINNs) and their capabilities in addressing challenges faced by traditional computational approaches. Discover how PINNs can efficiently handle high-dimensional problems governed by parameterized partial differential equations (PDEs) and incorporate noisy data in inverse problems. Learn about extensions such as conservative PINNs (cPINNs) and eXtended PINNs (XPINNs) designed for big data and large models. Examine various adaptive activation functions that enhance convergence in deep and physics-informed neural networks. Gain insights into diverse applications where PINNs outperform traditional methods, and understand their current limitations and future potential in advancing scientific computing.

Syllabus

Ameya Jagtap Enhancing Scientific Computing Through Physics informed Neural Networks


Taught by

Alan Turing Institute

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