Research Markets

A Bayesian Framework for Safe Optimal Control of Unknown Systems from Input-Output Data

Nov 2, 2026 Source ↗

Key Insights

As control engineering is applied to increasingly complex systems, learning-based control, which uses empirical data to reduce the reliance on first-principles models, has emerged as a promising parad.

Cite this synthesis DOI: 10.48550/arXiv.2611.03466 ↗

Show formatted citation

@misc{ acaciamarkets-research-a-bayesian-framework-for-safe-optimal-control-of-unknown-sys,
  title = { A Bayesian Framework for Safe Optimal Control of Unknown Systems from Input-Output Data },
  author = { Leszek },
  year = { 2026 },
  doi = { 10.48550/arXiv.2611.03466 },
  url = { http://arxiv.org/abs/2611.03466v1 },
  note = {Summarized and classified by AcaciaFund}
}

TY  - GEN
TI  - A Bayesian Framework for Safe Optimal Control of Unknown Systems from Input-Output Data
AU  - Leszek
PY  - 2026
DO  - 10.48550/arXiv.2611.03466
UR  - http://arxiv.org/abs/2611.03466v1
ER  -

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Overview

As control engineering is applied to increasingly complex systems, learning-based control, which uses empirical data to reduce the reliance on first-principles models, has emerged as a promising parad

Key Topics

Quantitative Modeling: A theme covered by this item within the markets context.

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Article Metadata

Evergreen Synthesized

quantitative-modeling market-microstructure

Market Microstructure

SQI 0.750