arXiv Machine Learning June 16, 2026 By Rajasree Sarkar, Arunava Banerjee, Sathya Aswath Govind Raju, Ishan Berk Altiner, Zongxuan Sun, Kenneth Kim, Chol-Bum Mike Keown

Data-driven Control with Real-time Uncertainty Compensation for Multi-Fuel Engines

Read the original on arXiv Machine Learning →

arXiv:2606. 16171v1 Announce Type: cross Abstract: Multi-fuel compression ignition (CI) engines offer superior power density and fuel flexibility.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

Jun 18

Learning-Based Decision Making for Combustion Phasing Control in Multi-Fuel CI Engines with Latent Fuel Reactivity Estimation

arXiv:2606. 18393v1 Announce Type: cross Abstract: Multi-fuel compression-ignition engines offer fuel flexibility but introduce uncertain, time-varying fuel reactivity, represented by cetane number (CN), which complicates cycle-to-cycle combustion-phasing control.

By Rajasree Sarkar, Aditya Satish Patil, Arunava Banerjee, Ihsan Berk Altiner, Zongxuan Sun, Kenneth Kim, Chol-Bum Mike Keown

reinforcement-learning

arXiv Machine Learning

Aug 6

Koopman-Based Nonlinear Identification and Model Predictive Control of a Turbofan Engine

arXiv:2604. 01730v2 Announce Type: replace Abstract: This paper investigates Koopman operator-based approaches for multivariable control of a two-spool turbofan engine.

By David Grasev

arXiv Machine Learning

Jul 10

Optimal uncertainty bounds for multivariate kernel regression under bounded noise: A Gaussian process-based dual function

arXiv:2603. 16481v3 Announce Type: replace Abstract: Non-conservative uncertainty bounds are essential for making reliable predictions about latent functions from noisy data, and thus, a key enabler for safe learning-based control.

By Amon Lahr, Anna Scampicchio, Johannes K\"ohler, Melanie N. Zeilinger

arXiv Machine Learning

Jul 7

Model Predictive Path Integral PID Control for Learning-Based Path Following

arXiv:2603. 29499v2 Announce Type: replace-cross Abstract: Classical proportional--integral--derivative (PID) control remains widely used in industrial control systems, while model predictive control (MPC) is actively studied to achieve higher performance for systems with nonlinear dynamics.

By Teruki Kato, Koshi Oishi, Seigo Ito

arXiv Machine Learning

Aug 4

Efficient nonlinear flame response modeling for propulsion thermoacoustic analysis using limited numerical data

arXiv:2409. 05885v2 Announce Type: replace Abstract: Characterizing nonlinear flame response is critical for predicting thermoacoustic instabilities in propulsion combustors, yet obtaining a comprehensive response map through high-fidelity simulations remains computationally prohibitive.

By Jiawei Wu, Teng Wang, Jiaqi Nan, Wang Han, Lijun Yang, Jingxuan Li

arXiv Machine Learning

Jun 30

A Bayesian latent Gaussian process framework for aerodynamic uncertainty quantification

arXiv:2606. 28871v1 Announce Type: cross Abstract: Predicting the aerodynamic performance (e.

By Geoffrey Davis, Ashwin Renganathan

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Learning-Based Decision Making for Combustion Phasing Control in Multi-Fuel CI Engines with Latent Fuel Reactivity Estimation

Koopman-Based Nonlinear Identification and Model Predictive Control of a Turbofan Engine

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