arXiv (All)
open access
AutoSINDy: Discovery of Nonlinear Dynamics with Automated Library Construction
Discovering governing equations from observational data remains a fundamental challenge in scientific modeling, particularly when the underlying mathematical structure is unknown. Traditional sparse identification methods like SINDy excel at discovering parsimonious models but require researchers to
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
- Replication Data for: Light Martini water accelerates sampling in coarse-grained molecular dynamics simulations#437870
- Blending experimental electron density information to protein-ligand interaction#1325758
- Tropical Cyclone Attributes in Sparse Autoencoder Features of an AI Weather Model#1413145
- AgentDiscover: Autonomous Discovery with Minimal Search Scaffolding#1201347
- The Agentic Scientist: Orchestrating Coding Agents for Automated Scientific (Co-)Discovery#1344756
Record · ID 1536125
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