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Daily Synthesis

No Data? No Problem: Geometry-Informed Neural Networks (GINNs) 🍸

GINNs are flipping the script on generative models by ditching data altogether. Instead, these AI models train directly from design constraints, making them perfect for engineering problems where data is scarce but formal requirements are plenty.

📐 What They Do:
GINNs generate shapes that meet specific design requirements (like attaching to interfaces or fitting within a region), all while enforcing diversity to create a structured, generative latent space.

⚙️ How It Works:
1️⃣ Constraints are translated into optimization goals using augmented Lagrangian methods.
2️⃣ Diversity is baked in by maximizing the distance between generated shapes.
3️⃣ Topological features like connected components and holes are controlled using persistent homology.

💡 Why It Matters:
No data? No problem! With GINNs, engineers can skip the hassle of collecting training datasets and build models directly from problem descriptions. Think PINNs meet topology optimization, but operating in neural fields.

🎯 Where to Dive Deeper:

• Arxiv (Arturs Berzins, Andreas Radler, Eric Volkmann, Sebastian Sanokowski, Sepp Hochreiter, Johannes Brandstetter)

• GitHub

• Project Page

• Andreas Radler’s LinkedIn Post

This could change how generative AI tackles engineering challenges. Stay tuned.

Sam Altman Wants AI to Solve ALL of Physics ✨

Yes, you read that right—Sam Altman, the CEO of OpenAI, isn’t just thinking about chatbots or generative models. He’s setting his sights on a bigger prize: solving ALL of physics.

"I guess if I had one personal thing to pick, I would say like, you know: go solve all of physics," he said, casually dropping this mind-blowing ambition.

And he’s not just interested in bits and pieces—he’s talking about understanding the universe and potentially manipulating it.

🔥 Why does it matter?
Altman believes a deeper understanding of physics could unlock entirely new ways to interact with and shape our reality. "The more we can understand about physics, the more we can manipulate the universe," he explained.

A few years ago, Altman’s focus was more on AI and genetics (remember his discussions with Elon Musk?). Now, his attention has shifted toward the ultimate frontier: cracking the mysteries of the cosmos.

🌌 What’s next?
If AI can tackle this monumental task, it could redefine science as we know it. Imagine AI-powered breakthroughs in quantum mechanics, relativity, or even unified field theory.

It’s ambitious, bold, and maybe even a little crazy—but isn’t that what makes it exciting?

With leaders like Altman dreaming big, the future of AI feels infinite.

Watch the complete interview: Check it out here

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Paper of the Day

• Geometry-Informed Neural Networks (link)

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Lecture of the Day

• PINNs for time series || Advancing Computational Fluid Dynamics with PINNs — CRUNCH Group at Brown University (link)

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