Physics Informed Neural Networks Courses and Certifications

Using Physics Informed Neural Networks to Build Digital Twins of Cardiac Hemodynamics

Explore physics-informed neural networks for creating patient-specific cardiac digital twins using non-invasive data to eliminate invasive procedures and uncertain treatments.

• YouTube
• 47 minutes
• Self-Paced
• Free Video

Steve Brunton

Physics Informed Neural Networks (PINNs) - Introduction and Applications

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Introduction to Physics Informed Neural Networks (PINNs): combining neural networks with physical laws to solve complex problems in fluid dynamics and beyond. Explores advantages, applications, and extensions of this innovative approach.

• YouTube
• 35 minutes
• Self-Paced
• Free Video

NPTEL-NOC IITM

Physics Informed Neural Networks - Introduction

Explore the fundamentals of Physics Informed Neural Networks in this concise introduction.

• YouTube
• 22 minutes
• Self-Paced
• Free Video

DataLearning@ICL

Physics-Informed Neural Networks in Medicine

Explore physics-informed neural networks' applications in medicine, focusing on innovative technologies combining data assimilation and machine learning.

• YouTube
• 57 minutes
• Self-Paced
• Free Video

NPTEL-NOC IITM

Physics Informed Neural Networks - BC Incorporation

Explore Physics Informed Neural Networks and learn techniques for incorporating boundary conditions in these advanced machine learning models.

• YouTube
• 18 minutes
• Self-Paced
• Free Video

DataLearning@ICL

WarpPINN: Cine-MR Image Registration with Physics-Informed Neural Networks

Explore WarpPINN, a physics-informed neural network for cardiac image registration. Learn how it quantifies local heart deformations, improving heart failure diagnosis through advanced strain estimation techniques.

• YouTube
• 58 minutes
• Self-Paced
• Free Video

Inside Livermore Lab

GPT-PINN and TGPT-PINN: Advanced Physics-Informed Neural Networks for Parametric PDEs

Explore GPT-PINN and TGPT-PINN for efficient parametric PDE solutions. Learn meta-learning techniques to reduce training time and network complexity while maintaining accuracy across parameter domains.

• YouTube
• 59 minutes
• Self-Paced
• Free Video

Inside Livermore Lab

Scientific Machine Learning through the Lens of Physics-Informed Neural Networks

Explore physics-informed neural networks (PINNs) as an alternative to traditional scientific computation, addressing limitations and showcasing applications in solving complex physical problems.

• YouTube
• 1 hour 3 minutes
• Self-Paced
• Free Video

APS Physics

Physics Informed Neural Networks and Neural Differential Equations

Explore scientific machine learning methods including Neural ODEs for approximating unknown differential equations from data and PINNs for solving known but challenging equations.

• YouTube
• 1 hour
• Self-Paced
• Free Video

NPTEL-NOC IITM

PINNs for Inverse Problems in Scientific Computing

Explore Physics-Informed Neural Networks (PINNs) and their application in solving inverse problems in scientific computing and engineering.

• YouTube
• 30 minutes
• Self-Paced
• Free Video

NPTEL-NOC IITM

Code Walkthrough for PINNs in Burgers Equation

Explore Physics-Informed Neural Networks (PINNs) applied to the Burgers equation through a detailed code walkthrough.

• YouTube
• 23 minutes
• Self-Paced
• Free Video

Inside Livermore Lab

Competitive Physics Informed Networks - Overcoming Limitations in PDE Solutions

Explore competitive physics-informed neural networks for solving PDEs with unprecedented accuracy. Learn about adversarial approaches and zero-sum games to achieve single-precision accuracy in numerical simulations.

• YouTube
• 1 hour 6 minutes
• Self-Paced
• Free Video

Alan Turing Institute

Improving the Variational Learning of Physics-Driven Neural Generative Models

Exploring advances in probabilistic emulators for parametric PDEs, including boundary condition enforcement and Random Grid Neural Processes for efficient engineering problem-solving.

• YouTube
• 25 minutes
• Self-Paced
• Free Video

Alan Turing Institute

PirateNets - Physics-Informed Deep Learning with Residual Adaptive Networks

Explore PirateNets: a novel deep learning architecture for solving PDE-governed problems, offering improved stability and efficiency over traditional physics-informed neural networks.

• YouTube
• 1 hour 3 minutes
• Self-Paced
• Free Video

NPTEL-NOC IITM

Formulation of PINN-Based Inverse Problems in Unsteady Heat Conduction - Lecture 65

Explore how Physics-Informed Neural Networks (PINNs) solve inverse problems in heat transfer, focusing on parameter estimation and PDE solutions in unsteady conduction scenarios.

• YouTube
• 19 minutes
• Self-Paced
• Free Video