Marco-A93/MF-KAN
🌐 Leverage Multifidelity Kolmogorov-Arnold Networks for efficient training with less high-fidelity data in PyTorch, enhancing model accuracy and performance.
What's novel
🌐 Leverage Multifidelity Kolmogorov-Arnold Networks for efficient training with less high-fidelity data in PyTorch, enhancing model accuracy and performance.
Code Analysis
0 files read · 5 roundsA partially implemented Python library for Multifidelity Kolmogorov-Arnold Networks (KANs) that combines high-fidelity and low-fidelity data but lacks core implementation files.
Strengths
The project demonstrates a clear architectural vision with well-organized modules for models, training, and utilities. The README accurately describes the intended functionality of combining multi-fidelity data for scientific machine learning tasks.
Weaknesses
Critical implementation files are missing or inaccessible, preventing evaluation of actual algorithms. Error handling is minimal, tests are absent, and the project appears incomplete with placeholder-like structures.
Score Breakdown
Signal breakdown
Innovation
Craft
Traction
Scope
Evidence
Commits
7
Contributors
2
Files
28
Active weeks
3
Repository
Language
Python
Stars
1
Forks
0
License
MIT