Accelerating Eigenvalue Dataset Generation via Chebyshev Subspace Filter
📰 ArXiv cs.AI
Accelerate eigenvalue dataset generation using Chebyshev subspace filters for neural eigenvalue methods, reducing computation time and improving efficiency
Action Steps
- Apply Chebyshev subspace filters to reduce the dimensionality of eigenvalue problems
- Use neural eigenvalue methods to solve eigenvalue problems with reduced computation time
- Generate large datasets of labeled eigenvalue problems using the proposed approach
- Evaluate the performance of the proposed method using metrics such as computation time and accuracy
- Compare the results with traditional solvers to demonstrate the efficiency of the proposed approach
Who Needs to Know This
Researchers and engineers working on machine learning and scientific computing can benefit from this approach to improve the efficiency of eigenvalue dataset generation
Key Insight
💡 Chebyshev subspace filters can significantly reduce the computation time required for eigenvalue dataset generation, enabling more efficient training of neural eigenvalue methods
Share This
🚀 Accelerate eigenvalue dataset generation using Chebyshev subspace filters! 📊
Key Takeaways
Accelerate eigenvalue dataset generation using Chebyshev subspace filters for neural eigenvalue methods, reducing computation time and improving efficiency
Full Article
Title: Accelerating Eigenvalue Dataset Generation via Chebyshev Subspace Filter
Abstract:
arXiv:2510.23215v2 Announce Type: replace-cross Abstract: Eigenvalue problems are among the most important topics in many scientific disciplines. With the recent surge and development of machine learning, neural eigenvalue methods have attracted significant attention as a forward pass of inference requires only a tiny fraction of the computation time compared to traditional solvers. However, a key limitation is the requirement for large amounts of labeled data in training, including operators an
Abstract:
arXiv:2510.23215v2 Announce Type: replace-cross Abstract: Eigenvalue problems are among the most important topics in many scientific disciplines. With the recent surge and development of machine learning, neural eigenvalue methods have attracted significant attention as a forward pass of inference requires only a tiny fraction of the computation time compared to traditional solvers. However, a key limitation is the requirement for large amounts of labeled data in training, including operators an
DeepCamp AI