Spectral-inspired Operator Learning with Limited Data and Unknown Physics
📰 ArXiv cs.AI
Learn to model complex systems with limited data using Spectral-Inspired Neural Operator (SINO), a novel approach that doesn't require explicit PDE terms or large datasets.
Action Steps
- Implement SINO using PyTorch or TensorFlow to model complex systems
- Use SINO to learn PDE dynamics from 2-5 trajectories without explicit PDE terms
- Compare SINO's performance with existing neural PDE solvers on benchmark datasets
- Apply SINO to real-world problems with limited data and unknown physics
- Test SINO's robustness to noise and missing data in the input trajectories
Who Needs to Know This
Researchers and engineers working on neural PDE solvers and operator learning can benefit from SINO, as it enables modeling of complex systems with limited data and unknown physics.
Key Insight
💡 SINO can model complex systems from just 2-5 trajectories without requiring explicit PDE terms, making it a powerful tool for operator learning with limited data.
Share This
🚀 Introducing SINO: a novel neural operator that learns complex systems from limited data without explicit PDE terms! 🤯 #neuralPDEs #operatorlearning
Key Takeaways
Learn to model complex systems with limited data using Spectral-Inspired Neural Operator (SINO), a novel approach that doesn't require explicit PDE terms or large datasets.
Full Article
Title: Spectral-inspired Operator Learning with Limited Data and Unknown Physics
Abstract:
arXiv:2505.21573v3 Announce Type: replace-cross Abstract: Learning PDE dynamics from limited data with unknown physics is challenging. Existing neural PDE solvers either require large datasets or rely on known physics (e.g., PDE residuals or handcrafted stencils), leading to limited applicability. To address these challenges, we propose Spectral-Inspired Neural Operator (SINO), which can model complex systems from just 2-5 trajectories, without requiring explicit PDE terms. Specifically, SINO au
Abstract:
arXiv:2505.21573v3 Announce Type: replace-cross Abstract: Learning PDE dynamics from limited data with unknown physics is challenging. Existing neural PDE solvers either require large datasets or rely on known physics (e.g., PDE residuals or handcrafted stencils), leading to limited applicability. To address these challenges, we propose Spectral-Inspired Neural Operator (SINO), which can model complex systems from just 2-5 trajectories, without requiring explicit PDE terms. Specifically, SINO au
DeepCamp AI