Back to feed
arXiv cs.LG
arXiv cs.LG
7/7/2026
The user wants me to rewrite a news headline for a mobile feed based on the original title and summary.

The user wants me to rewrite a news headline for a mobile feed based on the original title and summary.

Original: LiNO: Lifting based multiresolution neural operator

Short summary

Researchers propose LiNO, a neural operator using wavelet lifting schemes to learn multiscale features in differential equations. The method handles both coarse and fine-scale structures simultaneously through adaptive, invertible multiresolution decomposition. Benchmarks on physics simulations (Navier-Stokes, Poisson equations) show LiNO outperforms existing neural operators on multiscale and chaotic systems.

  • •Wavelet lifting scheme enables adaptive multiresolution operator learning
  • •Exactly invertible construction preserves information across scales
  • •Strong performance on physics benchmarks: Darcy flow, Navier-Stokes, Allen-Cahn, Gray-Scott

Generated with AI, which can make mistakes.

#research-breakthrough#ai-tools
Read full article at arXiv cs.LG

Is this a good recommendation for you?

Comments

Failed to load comments. Please try again.

Explore more

LLT: Local Linear Transformer for PDE Operator Learning
arXiv cs.LG
Neural Operator Surrogates for Two-Dimensional Neutron Flux Estimation
arXiv cs.LG
Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions
arXiv cs.LG
The original title is "Flowing vs. Thinking: How Liquid Neural Networks Diverge from LLMs"
Dev.to