arXiv Machine Learning

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

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

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

arXiv Machine Learning

Jun 9

Bayesian Optimization of a Multi-Product Chemical Reactor Using Composite Models and Partial Physics Knowledge

arXiv:2606. 08611v1 Announce Type: cross Abstract: We study data-driven real-time economic optimization of a multi-product chemical reactor when no reliable first-principles model is available beyond a steady-state energy balance.

By Liqiu Dong, Marta Zag\'orowska, Mehmet Mercang\"oz

benchmarks

arXiv AI

Jul 2

GPU-Parallel Linearization Error Bounds for Real-Time Robust Optimal Control of Nonlinear and Neural Network Dynamics

arXiv:2607. 01203v1 Announce Type: cross Abstract: This paper studies real-time robust optimal control for uncertain nonlinear systems, where linear time-varying (LTV) approximations make planning tractable but require sound linearization error bounds (LEBs) to guarantee robust constraint satisfaction.

By Jeffrey Fang, Keyi Shen, Anutam Srinivasan, Glen Chou

arXiv Machine Learning

Jul 13

Entropy-Constrained Machine Learning with Residual Data Augmentation for Modeling Chemical Kinetics

arXiv:2607. 09582v1 Announce Type: cross Abstract: We present a physics-constrained machine learning framework for accelerating the direct numerical simulation (DNS) of turbulent reacting flows.

By Okezzi Ukorigho, Opeoluwa Owoyele

arXiv Machine Learning

Jul 17

Lyapunov Guidance: A Unified Framework for Stabilizing Generative Flows

arXiv:2607. 14272v1 Announce Type: new Abstract: Flow matching has emerged as an effective framework for learning complex data distributions, but adapting pretrained flow models to new tasks often requires computationally expensive retraining.

By Jingdong Zhang, Xinze Li, Yize Jiang, Luan Yang, Minkai Xu, Junhong Liu

diffusion reinforcement-learning benchmarks

arXiv Machine Learning

Jul 17

Modeling and Control of Deep Sign-Definite Dynamics with Application to Hybrid Powertrain Control

arXiv:2509. 19869v2 Announce Type: replace-cross Abstract: Data-driven control increasingly relies on deep models for complex systems whose first-principles models are difficult to obtain.

By Teruki Kato, Ryotaro Shima, Kenji Kashima

arXiv Machine Learning

Jul 22

Thermodynamics-Informed Input Reparameterization for Neural Prediction of Real-Fluid Thermodynamic Properties in Supercritical Combustion

arXiv:2607. 19241v1 Announce Type: new Abstract: Real-fluid thermodynamic property evaluation is a major computational cost in supercritical combustion simulations.

By Haoze Zhang, Han Li, Ke Xiao, Yangchen Xu, Runze Mao, Zhi X. Chen

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