arXiv (All)
open access
Weak-Form Recovery of Stochastic Generators
60h1065c30chaotic-dynamicsdata-analysisdynamical-systemsmachine-learningmathematical-physicsmethodology
Data-driven discovery of governing equations is well developed for deterministic systems but much less so for stochastic ones. We present a sparse-symbolic algorithm that recovers both the drift $b(x)$ and the diffusion $a(x)$ of a stochastic differential equation from a single discretely sampled tr
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
- Stability Estimates for the Inverse Recovery of Drift and Diffusion Point Sources in Stochastic Parabolic Equations#1083491
- Free-Probabilistic State Evolution and Random Matrix Discrepancy#999518
- Enhanced Dissipation under Variable Growth: Stochastic $p(x)$-Laplace Equations with Transport Noise#1356578
- SLIM: Stochastic Learning and Inference in Overidentified Models#1215358
- Discrete Diffusion Inference-Time Control with Nested Sequential Monte Carlo#471237
Record · ID 1399817
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