Physics-Informed Neural Networks (PINNs) - Conor Daly | Podcast #120

💌 My weekly science newsletter - https://jousef.substack.com/ 💻 Full tutorial: https://www.youtube.com/watch?v=G_hIppUWcsc Physics-Informed Neural Networks (PINNs) integrate known physical laws into neural network learning, particularly for solving differential equations. They embed these laws into the network's loss function, guiding the learning process beyond just data fitting. This integration helps the network predict solutions that are not only data-driven but also align with physical principles, making PINNs especially useful in fields like fluid dynamics and heat transfer. By blending data with established physics, PINNs offer more accurate and robust predictions, especially in data-scarce scenarios. ONLINE PRESENCE ================ 🌍 My website - http://jousefmurad.com/ 💌 My weekly science newsletter - https://jousef.substack.com/ 📸 Instagram - https://www.instagram.com/jousefmrd/ 🐦 Twitter - https://twitter.com/Jousefm2 SUPPORT MY WORK ================= 🧠 Subscribe for more free videos: https://bit.ly/2RLmMxq 👉 Support my Channel: https://www.jousefmurad.com/#/portal/... 👕 Science Merch: https://engineered-mind.creator-sprin... CONTACT: ———————— If you need help or have any questions or want to collaborate feel free to reach out to me via email: [email protected] #pinns #mathworks #engineering Podcast Recorded: March, 4th 2024 - Subscriber Release Count: 31,484.