Update README.md

README.md

@@ -16,19 +16,6 @@ size_categories:
 - 1GB<n<10GB
 ---
 
-# PIFNO-LAW: Learned Adaptive Weighting for Physics-Informed Fourier Neural Operators
-
-Official dataset and documentation for **PIFNO-LAW**, a novel dual-operator framework designed to solve partial differential equations (PDEs) with discontinuous solutions (shocks) under limited-data scenarios. This repository focuses on the open-source **Burgers' Equation** benchmarks.
-
-## 📌 Overview
-
-Physics-informed Fourier neural operators (PIFNO) show great promise for scientific machine learning but frequently struggle with sharp discontinuities. High localized residuals at shock fronts tend to destabilize training. **PIFNO-LAW** overcomes this limitation by introducing an auxiliary Fourier Neural Operator that learns an optimal, dynamic weight field for the physics loss.
-
-### Key Innovations
-
-- **Dual-Operator Design**: An auxiliary FNO, conditioned on the local solution and its derivatives, identifies shocks dynamically.
-- **Dual-Penalty Regularization**: A novel objective that maintains a robust penalty in smooth regions while proactively suppressing chaotic residuals at shocks, replacing unstable minimax/adversarial training paradigms.
-- **Data Efficiency**: Specifically designed for robust "few-shot" learning where high-fidelity simulation data is sparse.
 
 ## 📊 Dataset Description
 
@@ -48,16 +35,3 @@ This dataset provides comprehensive one-dimensional inviscid Burgers' equation s
   - `x`: Spatial grid
   - `t`: Temporal grid
   - `u`: Scalar solution field
-
-## 📖 Citation
-
-If you use this dataset or the PIFNO-LAW architecture in your research, please cite our study:
-
-```bibtex
-@article{Hu2026PIFNOLAW,
-  title={Learned Adaptive Weighting for Physics-Informed Fourier Neural Operators: Solving Discontinuous PDEs based on Limited Data},
-  author={Hu, Xin and An, Bo and Guan, Yongke and Xu, Liang and Yu, Min and Li, Dong},
-  journal={Unpublished},
-  year={2026}
-}
-```