Symbolic--KAN: Kolmogorov-Arnold Networks with Discrete Symbolic Structure for Interpretable Learning
Symbolic--KAN: Kolmogorov-Arnold Networks with Discrete Symbolic Structure for Interpretable Learning explores Symbolic-KANs embed discrete symbolic structure into neural networks for interpretable and scalable discovery of governing equations in scientific machine learning.. Commercial viability score: 7/10 in Interpretable AI.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisIn-Context Symbolic Regression for Robustness-Improved Kolmogorov-Arnold NetworksViability: 2.0
- Builds On ThisSINDy-KANs: Sparse identification of non-linear dynamics through Kolmogorov-Arnold networksViability: 3.0
- Builds On ThisMultilevel Training for Kolmogorov Arnold NetworksViability: 6.0
- Builds On ThisClifford Kolmogorov-Arnold NetworksViability: 3.0
- Builds On ThisKANFIS A Neuro-Symbolic Framework for Interpretable and Uncertainty-Aware LearningViability: 5.0