Скачать или смотреть PINN-ACS: The AI Breakthrough for Instant Eigenvalue Solving

🔥 Tired of slow, unstable PINNs for eigenvalue problems? Meet *PINN-ACS**—up to **500x faster* than standard PINNs with provable convergence!

In this video, you’ll learn how PINN-ACS fixes hidden layers and only optimizes the output, turning the problem into a *biconvex optimization**. Using **Alternating Convex Search**, it alternates between analytically solving eigenfunctions (via Moore-Penrose pseudoinverse) and updating eigenvalues—guaranteeing **monotonic loss reduction* and eliminating vanishing gradients, loss conflicts, and chaotic training.

✅ Solves Euler buckling, Helmholtz on L-shaped domains, thin plate vibrations
✅ Handles **any boundary condition**, even eigenvalues in boundary operators
✅ Matches high-fidelity finite difference results—accurate, robust, fast

No more training nightmares. Just **physics-aware AI that works**.

💡 Ideal for researchers, engineers, and ML enthusiasts in PDEs, structural dynamics, and wave physics.

🚀 **Open-source**—get the code now (link in description)!

👉 If you’re using PINNs for eigenvalue problems, this changes everything. *Like, Subscribe, and comment “PINN-ACS”* if you’re ready to supercharge your simulations! #AIforPhysics #PINN #Eigenvalues #ScientificAI #MachineLearning #Python #DeepLearning #Shorts
Read more on arxiv by searching for this paper: 2511.00792v1.pdf