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Fine-Tuning of Neural Network Approximate MPC without Retraining via Bayesian Optimization
; ; ; ; ;
2025 & 2026
Submitted on 16 Dec 2025 (v1), last revised 16 Jan 2026 (this version, v3)
Online
DOI: 10.48550/arXiv.2512.14350
Einrichtungen
- Lehrstuhl für Data Science im Maschinenbau (422610)
- Graduiertenkolleg UnRAVeL (080060)
- RWTH Center for Artificial Intelligence (121004)
Projekte
- GRK 2236 - GRK 2236: Unsicherheit und Randomisierung in Algorithmen, Verifikation und Logik. (282652900) (282652900)
- DFG project G:(GEPRIS)315036061 - Ereignisbasierte drahtlose Regelung fuer Cyber-physikalische Systeme (315036061) (315036061)
- DFG project G:(GEPRIS)273882191 - SPP 1914: Cyber-Physical Networking (CPN) (273882191) (273882191)
External link: 
Fulltext by arXiv.org
Dokumenttyp
Preprint
Format
online
Sprache
English
Externe Identnummern
arXiv: arXiv:2512.14350
Interne Identnummern
RWTH-2026-02424
Datensatz-ID: 1029590
Beteiligte Länder
Germany
Related:
Contribution to a conference proceedings
Fine-Tuning of Neural Network Approximate MPC without Retraining via Bayesian Optimization
CoRL Workshop on Safe and Robust Robot Learning for Operation in the Real World : CoRL 2024 Workshop SAFE-ROL
CoRL Workshop on Safe and Robust Robot Learning for Operation in the Real World, CoRL 2024 Workshop SAFE-ROL, MunichMunich, Germany, 9 Nov 2024 - 9 Nov 2024
Conference on Robot Learning 2024, CoRL 2024, MunichMunich, Germany, 6 Nov 2024 - 9 Nov 2024
[1]-12 (2024) [10.18154/RWTH-2025-02289]
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