arXiv (All)
open access
PACE-FNO: Physics-Aligned Canonical Equivariance for Fourier Neural Operators
Neural operators are often tested on states that differ physically from training data. A distinct failure occurs when the physical dynamics are unchanged but the observed coordinate frame differs from training. PACE-FNO addresses this case by estimating the frame, predicting after pulling the field
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Fast-forward prediction of lattice Boltzmann dynamics with physics-informed neural operators.#631102
- Structure-preserving Fourier Neural Operators for Long-time Cahn-Hilliard Dynamics under Coarse Temporal Supervision#1144020
- Drift Happens: An Empirical Study of Neural Architecture Robustness to Temporal Distribution Shift#346528
- Transferable Physics-Informed Representations via Closed-Form Head Adaptation#126512
- Real vs. Complex Spectral Bases for Neural Operators: The Role of Green's Function Alignment#925186
Record · ID 1117425
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive