arXiv (All)
open access
Turnpike properties in nonlinear system identification
We analyze the problem of learning general discrete-time nonlinear state-space models using the simulation error minimization (SEM) method. In this setting, model parameters are typically learned by minimizing the mismatch between simulated and measured outputs over a training dataset, or shorter su
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
- Discretization-independent operator learning for partial differential equations#1048485
- Practical Algebraic Parameter Estimation for Noisy Data via Gaussian Process Regression#1104650
- Improving Reduced-Order Rotating Detonation Engine Models with Data Assimilation and Machine Learning#1110611
- Fast Convergence for High-Order ODE Solvers in Diffusion Probabilistic Models#999662
- Residual-augmented flow matching operators for probabilistic partial differential equations#973008
Record · ID 822477
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