What type of study is this?

This is a Experimental Study study.

October 20, 2025Open Access

F-Adapter: Frequency-Adaptive Parameter-Efficient Fine-Tuning in Scientific Machine Learning

View Full Paper

Ask AI

Bookmark

Authors

HZHangwei Zhang CKChunsheng KangTianjin Medical University General Hospital SWShilong WangBeijing Institute of Technology

Discussion

Member takes

Implication

This systematic study introduces frequency-adaptive tuning for Large Operator Models, suggesting significant improvements in performance and generalization in complex physical systems.

Key Points

F-Adapter significantly improves spectral fidelity in 3D Navier-Stokes benchmarks, outperforming existing techniques.
Theoretical analysis reveals low-rank adaptation introduces approximation errors in Fourier layers, while adapters maintain approximation capacity.
Parameter allocation in F-Adapter is based on spectral complexity, optimizing module dimensions for lower and higher frequencies.
This approach marks the first exploration of parameter-efficient fine-tuning in the context of scientific machine learning.

Cite This Study

Zhang et al. (2025) studied this question.

synapsesocial.com/papers/68f6196ee0bbbc94fac3656b https://doi.org/10.48550/arxiv.2509.23173

View Full Paper

Ask AI

Bookmark

Implication

This systematic study introduces frequency-adaptive tuning for Large Operator Models, suggesting significant improvements in performance and generalization in complex physical systems.

F-Adapter: Frequency-Adaptive Parameter-Efficient Fine-Tuning in Scientific Machine Learning

Discussion

Member takes

Implication

Key Points

Cite This Study

F-Adapter: Frequency-Adaptive Parameter-Efficient Fine-Tuning in Scientific Machine Learning

Discussion

Member takes

Implication

Key Points

Cite This Study

Also Consider

Also Consider