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AI Engineering
DeepMind Blog·Oct 24·5 min read
Discovering new solutions to century-old problems in fluid dynamics
TL;DR
For centuries, fluid dynamics has faced challenges due to complex equations that can lead to unrealistic scenarios called singularities. A new family of mathematical blow ups has been introduced, leveraging AI techniques to address these longstanding issues.
✦ Why It Matters
Engineers can apply these new mathematical insights to improve fluid dynamics models in their projects.
Key Takeaways
- Investigate the use of PINNs in your own fluid dynamics research.
- Consider the implications of unstable singularities in your mathematical models.
- Stay updated on AI advancements in mathematical research.
- Collaborate with interdisciplinary teams to tackle complex equations.
How It Works
The method employs Physics-Informed Neural Networks (PINNs), which are trained to satisfy physical equations while minimizing the residual error. This allows the model to discover elusive singularities by embedding mathematical insights directly into the training process.