arXiv Machine Learning

A Reinforcement-Learning-Augmented Liquid-Fueled Reactor Network Model for Predicting Lean Blowout in Gas Turbine Combustors

arXiv:2607. 19281v1 Announce Type: new Abstract: This study introduces a reinforcement learning (RL) framework for generating optimal liquid-fueled reactors to improve lean blowout (LBO) predictions in gas turbine combustors.

arXiv Machine Learning

Jun 16

Machine Learning-Driven Chemical Reactor Network Modeling of the Sandia-D Flame

arXiv:2606. 14729v1 Announce Type: cross Abstract: Turbulent combustion simulations are crucial for many scientific and engineering systems.

By Nicolas J. Tricard, Benjamin C. Koenig, Sili Deng

diffusion

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 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 AI

Jul 10

Reaction-network reasoning with frontier models for experimentally confirmed catalyst-selectivity hypotheses

arXiv:2607. 08003v1 Announce Type: cross Abstract: Catalysts are essential for sustainable chemical manufacturing, yet discovering novel architectures remains a bottleneck dominated by trial-and-error experimentation and computationally intensive screening.

By Sutanay Choudhury, Anwesha Banerjee, Udishnu Sanyal, Jorin Dawidowicz, Chiezugolum Ijeoma Odilinye, Jesun Firoz, Liney Arnadottir, Simone Raugei, Johannes Lercher, Arnab Dutta

llms

arXiv Machine Learning

Jun 9

Deep reinforcement learning for process design: Review and perspective

arXiv:2308. 07822v2 Announce Type: replace Abstract: The transformation towards renewable energy and feedstock supply in the chemical industry requires new conceptual process design approaches.

By Qinghe Gao, Artur M. Schweidtmann

agents reinforcement-learning benchmarks

arXiv Machine Learning

Aug 10

The Challenges of Using Reinforcement Learning for Controlling Industrial Energy Systems

arXiv:2605. 31044v2 Announce Type: replace Abstract: Reinforcement learning has shown promising results for optimizing the control of industrial energy systems, yet most existing studies remain limited to the application in simulation environments.

By Tobias Lademann, Th\'eo Vincent, Jan Peters, Matthias Weigold

reinforcement-learning

arXiv Machine Learning

Aug 10

CrystalGRPO: Target-Aligned and Coverage-Preserving Reinforcement Learning for Flow-Based Crystal Structure Prediction

arXiv:2608. 06582v1 Announce Type: new Abstract: Flow-based generative models can efficiently produce candidate structures for crystal structure prediction (CSP), but their pretrained objectives do not directly optimize downstream target recovery.

By Kaixiang Su, Hongfei Xue, Qiang Zhu

reinforcement-learning

arXiv Machine Learning

Jun 25

Supervised Reinforcement Learning for the Coordination of Distributed Energy Resources

arXiv:2606. 24947v1 Announce Type: new Abstract: The increasing integration of distributed energy resources (DERs) is crucial for power system decarbonization, yet unlocking DERs' flexibility is challenged by their inherent uncertainties and modelling complexity.

By Haoyuan Deng, Yihong Zhou, Thomas Morstyn, Yi Wang

llms reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

2d ago

Learning to Run Power Networks: Effective AlphaZero-inspired Topological Control

arXiv:2608. 14114v1 Announce Type: new Abstract: As the integration of volatile renewable energy sources increases the strain on modern power grids, the use of Reinforcement Learning (RL) for autonomous topological reconfiguration has emerged as a promising research field to keep strained grids stable and operational.

By Lukas Zetto, Benjamin Sch\"afer, Qiong Huang

agents reinforcement-learning

arXiv Machine Learning

Jun 25

Uncertainty-aware reinforcement learning for chemical language models

arXiv:2606. 24990v1 Announce Type: new Abstract: Reinforcement Learning (RL) has become a powerful paradigm for de novo molecular design, enabling Chemical Language Models (CLMs) to navigate and explore the chemical space while optimizing specific desired properties.

By Borja Medina, Jon Paul Janet

llms reinforcement-learning

arXiv Machine Learning

Jul 7

Sample-Efficient Pareto Front Modeling for Energy-Aware Reinforcement Learning Using Bayesian Optimization

arXiv:2607. 03140v1 Announce Type: new Abstract: Industrial automation increasingly demands control strategies that balance operational performance with strict energy efficiency requirements.

By Georg Sch\"afer, Jakob Rehrl, Stefan Huber, Simon Hirlaender

reinforcement-learning safety

arXiv Machine Learning

Jun 5

Multi-Fidelity Learning with Shallow Recurrent Decoders for Reactor Physics

arXiv:2606. 05202v1 Announce Type: cross Abstract: In reactor physics, neutronics can be treated with different fidelity levels, according to the needs of the user.

By Stefano Riva, Carolina Introini, J. Nathan Kutz, Antonio Cammi

diffusion benchmarks

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