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

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Manage episode 414567039 series 3245084
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://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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🌎 Website: ⁠http://jousefmurad.com/⁠

🌎 Technical Marketing for Your Business:

📥 Weekly free science insights newsletter:

🐤 Follow me on Twitter: @jousefm2

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  continue reading

130 episodes

Artwork
iconShare
 
Manage episode 414567039 series 3245084
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://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

130 episodes

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