Physics-Informed Neural Networks (PINNs) - Conor Daly | Podcast #120 The Engineered-Mind Podcast | Engineering, AI & Technology podcast

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The Engineered-Mind Podcast | Engineering, AI & Technology « »
Physics-Informed Neural Networks (PINNs) - Conor Daly | Podcast #120

1y ago 1:05:00

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Content provided by Jousef Murad. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Jousef Murad or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

💻 Full tutorial: • Physics-Informed Neural Networks (PIN... 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.
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147 episodes

#Podcasting Education #Jousef Murad

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

The Engineered-Mind Podcast | Engineering, AI & Technology

19 subscribers

published 1y ago

Share

MP3•Episode home

Content provided by Jousef Murad. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Jousef Murad or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

💻 Full tutorial: • Physics-Informed Neural Networks (PIN... 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.
—————————————————————————————

🌎 Website: ⁠http://jousefmurad.com/⁠

🌎 Technical Marketing for Your Business:

📥 Weekly free science insights newsletter:

🐤 Follow me on Twitter: @jousefm2

📷 Follow me on Instagram: @jousefmrd

… continue reading

147 episodes

#Podcasting Education #Jousef Murad

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