A curated, living collection of papers, frameworks, and resources at the intersection of Large Language Models (LLMs) and Control Systems / Power Electronics.
As LLMs demonstrate emergent reasoning and planning capabilities, their integration with rigorous control theory—from classical PID design to nonlinear optimal control and power electronics—is rapidly evolving. This repository tracks the frontier.
Comprehensive surveys covering LLMs, RL, and AI methods for control systems and power electronics.
| Title | Authors | Year | Venue | TL;DR |
|---|---|---|---|---|
| When {{Control Meets Large Language Models | Nosrati, Komeil, Tepljakov, Aleksei, Belikov, Juri et al. | 2026 | — | Explores the bidirectional relationship between LLMs and control: how LLMs advance control design while control theory helps improve LLM alignment and interpretability. |
| {{LLM and AI Agents | — | 2026 | — | A comprehensive survey on LLM and AI agents for autonomous systems, covering the transition from modular rule-based to reasoning-driven agent-based approaches. |
| Synthesizing {{Interpretable Control Policies | Bosio, Carlo, Mueller, Mark W. | 2026 | — | The combination of Large Language Models (LLMs), systematic evaluation, and evolutionary algorithms has enabled breakthr... |
| Using {{Large Language Models | Ouhib, Lamia | 2026 | — | The integration of Large Language Models (LLMs) into control system design has evolved rapidly from basic prompt-based e... |
| Large {{Language Models | Bernadić, Alen, Kujundžić, Goran, Primorac, Ivana | 2025 | International Journal of Innovative Solutions in E | It is suggested that LLMs can significantly enhance the efficiency and reliability of power system operations, paving the way for more intelligent and adaptive energy management systems. |
| Multi-{{Agent Collaboration Mechanisms | Tran, Khanh-Tung, Dao, Dung, Nguyen, Minh-Duong et al. | 2025 | — | This work provides an extensive survey of the collaborative aspect of MASs and introduces an extensible framework to guide future research, and identifies key lessons learned, open challenges, and potential research directions of MASs towards artificial collective intelligence. |
| Empowering Scientific Discovery with Explainable Small Domain-Specific and Large Language Models | Yu, Hengjie, Wang, Yizhi, Cheng, Tao et al. | 2025 | Artificial Intelligence Review | As artificial intelligence (AI) increasingly integrates into scientific research, explainability has become a cornerston... |
| Exploring the Role of Large Language Models in the Scientific Method: From Hypothesis to Discovery | Zhang, Yanbo, Khan, Sumeer A., Mahmud, Adnan et al. | 2025 | npj Artificial Intelligence | For LLMs to serve as relevant and effective creative engines and productivity enhancers, their deep integration into all steps of the scientific process should be pursued in collaboration and alignment with human scientific goals, with clear evaluation metrics. |
| A {{Review | Chen, Peng, Zhao, Jianfeng, Liu, Kangli et al. | 2024 | IEEE Transactions on Industry Applications | This article provides a summary of existing published research papers on reinforcement learning control for power electronic converters, and introduces the existing RL control strategies for various power electronic converter topologies. |
| {{MapCoder | Islam, Md Ashraful, Ali, Mohammed Eunus, Parvez, Md Rizwan | 2024 | — | A new approach to code generation tasks leveraging multi-agent prompting that uniquely replicates the full cycle of program synthesis as observed in human developers, and consistently delivers superior performance across various programming languages and varying problem difficulties. |
| A Survey on {{LLM-based | Li, Xinyi, Wang, Sai, Zeng, Siqi et al. | 2024 | Vicinagearth | A comprehensive survey of LLM-based multi-agent systems is presented, offering a systematic review of these studies, and a general structure encompassing five key components: profile, perception, self-action, mutual interaction, and evolution is synthesized. |
| A {{Review | Núñez-Molina, Carlos, Mesejo, Pablo, Fernández-Olivares, Juan | 2024 | ACM Comput. Surv. | This article reviews AP, RL and hybrid methods for solving Sequential Decision Processes (SDPs), focusing on their knowledge representation: symbolic, subsymbolic, or a combination, and concludes that neurosymbolic AI poses a promising approach for SDM, since it combines AP and RL with a hybrid knowledge representation. |
| Mathematical Discoveries from Program Search with Large Language Models | Romera-Paredes, Bernardino, Barekatain, Mohammadamin, Novikov, Alexander et al. | 2024 | Nature | This work introduces FunSearch (short for searching in the function space), an evolutionary procedure based on pairing a pretrained LLM with a systematic evaluator that demonstrates the effectiveness of this approach to surpass the best-known results in important problems, pushing the boundary of existing LLM-based approaches. |
| Applying {{Large Language Models | Xia, Yuchen, Jazdi, Nasser, Weyrich, Michael | 2024 | atp magazin | The studies illustrate the ability of LLMs to manage versatile tasks and interface with digital twins and automation systems, indicating that efficiency and productivity improvements can be achieved by strategically deploying LLM technologies in industrial settings. |
| Deep {{Reinforcement Learning | Li, Yuanzheng, Yu, Chaofan, Shahidehpour, Mohammad et al. | 2023 | Proceedings of the IEEE | This article presents a comprehensive literature survey on DRL and its applications in smart grid operations, and reviews various DRL techniques as well as their extensions developed to cope with emerging issues in the smart grid, including optimal dispatch, operational control, electricity market, and other emerging areas. |
Papers applying LLMs directly to controller synthesis, PID/H∞ tuning, power converter design, and automation.
| Title | Authors | Year | Venue | TL;DR |
|---|---|---|---|---|
| When Control Meets Large Language Models: {{From | Nosrati, Komeil, Tepljakov, Aleksei, Belikov, Juri et al. | 2026 | — | While large language models (LLMs) are transforming engineering and technology through enhanced control capabilities and... |
| {{ControlAgent | Guo, Xingang, Keivan, Darioush, Syed, Usman et al. | 2025 | — | Control system design is a crucial aspect of modern engineering with far-reaching applications across diverse sectors in... |
| Crucible: {{Quantifying | Jia, Lianchen, Li, Chaoyang, Houde, Qian et al. | 2025 | — | This work introduces Crucible, an agent that employs an LLM-driven, multi-level expert simulation to turn algorithms and defines a formalized metric to quantitatively evaluate their Tuning Potential, providing a new dimension for algorithm analysis and design. |
| {{LLM-based PID | Kamenko, Ilija, Ilic, Slobodan, Congradac, Velimir | 2025 | 2025 10th {{International Conference | This study explores the application of Large Language Models (LLMs) to optimize Proportional-Integral-Derivative (PID) c... |
| Linear {{Feedback Control Systems | Karn, Rupesh Raj | 2025 | — | This paper presents a novel approach that draws parallels between the iterative prompt optimization process in LLMs and feedback control systems, providing a mathematical foundation for integrating linear feedback control mechanisms with LLMs. |
| From {{Natural Language | Li, Shihao, Li, Jiachen, Xu, Jiamin et al. | 2025 | — | We present textsc\S2C\ (Specification-to-Certified-Controller), a multi-agent framework that maps natural-language requi... |
| Towards {{Ethical AI | Lin, Fanfan, Wilson, Peter, Li, Xinze et al. | 2025 | — | Artificial intelligence (AI) is rapidly transforming power electronics, with AI-related publications in IEEE Power Elect... |
| Automating the Practice of Science: {{Opportunities | Musslick, Sebastian, Bartlett, Laura K., Chandramouli, Suyog H. et al. | 2025 | — | By discussing the motivations behind automated science, analyzing the hurdles encountered, and examining its implications, this article invites researchers, policymakers, and stakeholders to navigate the rapidly evolving frontier of automated scientific practice. |
| {{AgenticControl | Narimani, Mohammad, Emami, Seyyed Ali | 2025 | — | Traditional control system design, reliant on expert knowledge and precise models, struggles with complex, nonlinear, or... |
| {{SmartControl | Tohma, Kadir, Okur, Halil İbrahim, Gürsoy-Demir, Handan et al. | 2025 | SoftwareX | SmartControl is an interactive PID controller design tool powered by the novel integration of Large Language Model (LLM)... |
| {{LLM-Agent-Controller | Zahedifar, Rasoul, Mirghasemi, Sayyed Ali, Baghshah, Mahdieh Soleymani et al. | 2025 | — | This study presents the LLM-Agent-Controller, a multi-agent large language model (LLM) system developed to address a wid... |
| {{LLM-controller | Zahedifar, Rasoul, Soleymani Baghshah, Mahdieh, Taheri, Alireza | 2025 | Robotics and Autonomous Systems | In this study, a dynamic adaptation of a robot controller is investigated using large language models (LLMs). We propose... |
| {{LLMs-guided | Zhou, Zhongchao, Lu, Yuxi, Zhu, Yaonan et al. | 2025 | — | With rapid advances in code generation, reasoning, and problem-solving, Large Language Models (LLMs) are increasingly ap... |
| Large {{Language Models | Dharmaji, Rahul | 2024 | — | This manuscript is comprised of two sections — automated code generation for Programmable Logic Controllers and vulnerab... |
| {{ControlAgent | Guo, Xingang, Keivan, Darioush, Syed, Usman Ahmed et al. | 2024 | — | Control system design is a crucial aspect of modern engineering with far-reaching applications across diverse sectors, i... |
| Design {{Automation | Hermanns, Kevin | 2024 | {{CIPS | The demand for power electronic solutions is growing rapidly. The number of skilled workers available in the labour mark... |
| Controller {{Design Automation | Li, Wanrong, Li, Sinan, Yuan, Huawei et al. | 2024 | IEEE Transactions on Power Electronics | Efficient tools for automating controller design are essential to meet the growing demands of power electronics (PE) app... |
| {{PE-GPT | Lin, Fanfan, Li, Xinze, Lei, Weihao et al. | 2024 | IEEE Transactions on Industrial Electronics | This study presents a pioneering approach to establish a multimodal LLM tailored for PE design applications, named PE-GPT, and proposes a hybrid framework that integrates an LLM agent with metaheuristic algorithms, Model Zoo, and Simulation Repository to enhance its multimodal processing capabilities and enables integration into the existing design workflow. |
| Physics-{{Informed LLM-Agent | Liu, Junhua, Lin, Fanfan, Li, Xinze et al. | 2024 | — | LLM-based autonomous agents have demonstrated outstanding performance in solving complex industrial tasks. However, in t... |
| Using {{ChatGPT | Liu, Quanrui, Yuan, Xibo, Busquets-Monge, Sergio et al. | 2024 | 2024 {{IEEE | Two customized generative pre-trained transformers are developed with a retrieval-augmented generation (RAG) framework to select the optimal topology and design the system's parameters with the predefined specifications. |
| Eureka: {{Human-Level Reward Design | Ma, Yecheng Jason, Liang, William, Wang, Guanzhi et al. | 2024 | — | Eureka is presented, a human-level reward design algorithm powered by LLMs that exploits the remarkable zero-shot generation, code-writing, and in-context improvement capabilities of state-of-the-art LLMs to perform evolutionary optimization over reward code. |
| Autonomous {{Industrial Control | Vyas, Javal, Mercangöz, Mehmet | 2024 | — | An innovative approach to industrial automation is proposed, introducing validation and reprompting architectures utilizing large language model (LLM)-based autonomous control agents that enables autonomous management of control tasks, adapting to unforeseen disturbances without human intervention. |
| {{LLM-Powered Multi-Actor System | Xavier, Midhun, Laikh, Tatiana, Patil, Sandeep et al. | 2024 | {{IECON | An innovative multiactor framework that harnesses the potential of LLMs to augment the functionalities of ICS by integrating conversational AI technologies, significantly improves human-machine interactions, enabling sophisticated analysis and visualization of intricate data sets. |
| Automated {{Companion Tool | Zhetessov, Aidar, Venkataramanan, Prof. Giri | 2024 | 2024 {{IEEE Energy Conversion Congress | Due to their inherent nonlinearity, time-variance, system uncertainties, and requirements of robustness, control of powe... |
| Efficient and {{Robust Controller Design Automation | Li, Wanrong, Li, Sinan, Yuan, Huawei et al. | 2023 | 2023 {{IEEE Energy Conversion Congress | The global penetration of power electronics (PE) has increased significantly, creating a pressing need for efficient and... |
| Towards Autonomous System: Flexible Modular Production System Enhanced with Large Language Model Agents | Xia, Yuchen, Shenoy, Manthan, Jazdi, Nasser et al. | 2023 | 2023 {{IEEE | A novel framework that combines large language models (LLMs), digital twins and industrial automation system to enable intelligent planning and control of production processes and demonstrates how the implemented prototype can handle un-predefined tasks, plan a production process, and execute the operations. |
Multi-agent LLM frameworks for autonomous control, industrial automation, and planning.
| Title | Authors | Year | Venue | TL;DR |
|---|---|---|---|---|
| Learning Environment and Dynamics Representations for Autonomous Robot Navigation - ProQuest | 2026 | — | Explore millions of resources from scholarly journals, books, newspapers, videos and more, on the ProQuest Platform.... | |
| Towards an {{AI | Gottweis, Juraj, Weng, Wei-Hung, Daryin, Alexander et al. | 2025 | — | Scientific discovery relies on scientists generating novel hypotheses that undergo rigorous experimental validation. To ... |
| Towards {{Agentic AI | Huang, Lifu, Koutra, Danai, Kulkarni, Adithya et al. | 2025 | Companion {{Proceedings | AI models for science [14] have the potential to harness large datasets, accelerate scientific discoveries and transform... |
| Al-{{Khwarizmi | Mower, Christopher E., Bou-Ammar, Haitham | 2025 | — | Al-Khwarizmi is introduced, a novel agentic framework for physical law discovery from data, which integrates foundational models with SINDy and demonstrates state-of-the-art performance compared to alternatives, reaching a 20 percent increase against the best-performing alternative. |
| Discovering State-of-the-Art Reinforcement Learning Algorithms | Oh, Junhyuk, Farquhar, Greg, Kemaev, Iurii et al. | 2025 | Nature | It is shown that it is possible for machines to discover a state-of-the-art RL rule that outperforms manually-designed rules, and suggest that the RL algorithms required for advanced artificial intelligence may soon be automatically discovered from the experiences of agents, rather than manually designed. |
| {{DrAgent | Saha, Barun Kumar, V, Aarthi, Naidu, O. D. | 2025 | 2025 {{International Conference | The results of performance evaluation indicate that DrAgent, in general, can significantly improve the accuracy of responses requiring numerical results, and the step-by-step reasoning of agentic workflow can potentially help power grid operators, both novice and expert, to learn or verify the process. |
| Towards Agentic Science for Advancing Scientific Discovery | Xin, Hongliang, Kitchin, John R., Kulik, Heather J. | 2025 | Nature Machine Intelligence | Artificial intelligence is transforming scientific discovery through (semi-)autonomous agents capable of reasoning, plan... |
| {{V2V-LLM | Chiu, Hsu-kuang, Hachiuma, Ryo, Wang, Chien-Yi et al. | 2025 | ICRA 2026 | First to integrate multimodal LLMs into V2V cooperative autonomous driving, proposing V2V-QA dataset and V2V-LLM model for cooperative perception and planning. |
| Autonomous {{Code Evolution Meets NP-Completeness | Yu, Cunxi, Liang, Rongjian, Ho, Chia-Tung et al. | 2025 | — | Large language models (LLMs) have recently shown strong coding abilities, enabling not only static code generation but a... |
| {{MetaGPT | Hong, Sirui, Zhuge, Mingchen, Chen, Jiaqi et al. | 2024 | — | MetaGPT is introduced, an innovative framework that incorporates efficient human workflows as a meta programming approach into LLM-based multi-agent collaboration and leverages the assembly line paradigm to assign diverse roles to various agents, thereby establishing a framework that can effectively and cohesively deconstruct complex multi- agent collaborative problems. |
| Meta-{{Learning Augmented MPC | Lapandić, Dženan, Xie, Fengze, Verginis, Christos K. et al. | 2024 | IEEE Control Systems Letters | A major challenge in autonomous flights is unknown disturbances, which can jeopardize safety and cause collisions, espec... |
Reinforcement and deep reinforcement learning applied to power converters, smart grids, and robotic control.
| Title | Authors | Year | Venue | TL;DR |
|---|---|---|---|---|
| Reinforcement {{Learning-Based Control | Weber, Daniel, Lange, Jarren, Wallscheid, Oliver | 2026 | IEEE Journal of Emerging and Selected Topics in In | Reinforcement learning-based control allows a model-free, self-adaptive control approach with the possibility of non-lin... |
| {{TransformerMPC | Zinage, Vrushabh, Khalil, Ahmed, Bakolas, Efstathios | 2024 | — | Proposes TransformerMPC that uses Transformer attention mechanism to accelerate Model Predictive Control (MPC) by removing inactive constraints and providing better warm-start initialization, achieving up to 35x speedup. |
| Discovering {{Symbolic Policy | Kim, Soo Kyung, Song, Chihyeon, Chen, Weizhe et al. | 2023 | IFAC-PapersOnLine | We propose a learning framework for interpretable HVAC control in buildings using deep reinforcement learning (DRL). Our... |
| A {{Model-Free Switching | Qashqai, Pouria, Babaie, Mohammad, Zgheib, Rawad et al. | 2023 | IEEE Access | A novel model-free switching and control method for three-level neutral point clamped (NPC) converter using deep reinforcement learning (DRL), which exhibits superior adaptability to real-time changes and uncertainties, delivering more robust performance compared to similar conventional methods like MPC. |
| Discovering Symbolic Policies with Deep Reinforcement Learning | Landajuela, Mikel, Petersen, Brenden K., Kim, Sookyung et al. | 2021 | Proceedings of the 38th {{International Conference | Deep reinforcement learning (DRL) has proven successful for many difficult control problems by learning policies represe... |
Discovering interpretable policies: symbolic regression, decision trees, Lyapunov functions, explainable RL.
| Title | Authors | Year | Venue | TL;DR |
|---|---|---|---|---|
| {{Neural Optimization Machine | Li, Anran, Swensen, John P., Hosseinzadeh, Mehdi | 2025 | — | Proposes a Neural Optimization Machine (NOM) that transforms non-convex optimization problems in NN-based optimal control into neural network training problems with rigorous stability guarantees. |
| Deep {{Symbolic Optimization | Hayes, Conor F., Silva, Felipe Leno Da, Yang, Jiachen et al. | 2025 | — | This chapter provides a comprehensive overview of the DSO framework and illustrates its transformative potential for automating symbolic optimization in scientific discovery. |
| Constructive {{Symbolic Reinforcement Learning | Patrascu, Andrei T. | 2025 | — | A novel learning and planning framework that replaces traditional reward-based optimisation with constructive logical inference and presents a new direction for reinforcement learning grounded not in numeric optimisation, but in constructive logic and proof theory. |
| {{SkillTree | Wen, Yongyan, Li, Siyuan, Zuo, Rongchang et al. | 2025 | Proceedings of the AAAI Conference on Artificial I | Deep reinforcement learning (DRL) has achieved remarkable success in various domains, yet its reliance on neural network... |
| Model-Free Robust Underactuated Control of Inverted Pendulums | Zhu, Quanmin, Lei, Changyi, Shi, Baiyang et al. | 2025 | Journal of the Franklin Institute | This work proposes a type of model-free robust underactuated control (MFRUC) framework for improving the efficiency and ... |
| Why the {{Agent Made | Zuo, Rui, Khan, Simon, Wang, Zifan et al. | 2025 | — | VisionMask is a standalone explanation model trained end-to-end to identify the most critical regions in the agent's visual input that can explain its actions, hence preserving the agent's performance and integrity. |
| Towards {{Knowledge-Augmented Agents | Alansary, Reem, Ehab, Nourhan | 2024 | Proceedings of the 16th {{International Conference | This position paper argues that the decision-making abilities of such knowledge-augmented solvers transcend those of black-box function approximators alone as the former can generalize through inductive reasoning to behave optimally in unknown states and still remain fully or partially interpretable. |
| Global {{Lyapunov | Alfarano, Alberto, Charton, François, Hayat, Amaury | 2024 | Advances in Neural Information Processing Systems | — |
| {{SymbolicAI | Dinu, Marius-Constantin, Leoveanu-Condrei, Claudiu, Holzleitner, Markus et al. | 2024 | — | We introduce SymbolicAI, a versatile and modular framework employing a logic-based approach to concept learning and flow... |
| Meta-{{Learning-Based Adaptive Stability Certificates | Jena, Amit, Kalathil, Dileep, Xie, Le | 2024 | Proceedings of the AAAI Conference on Artificial I | This paper addresses the problem of Neural Network (NN) based adaptive stability certification in a dynamical system. Th... |
| Towards the {{Automatic Synthesis | Krishnan, Abhijeet, Martens, Chris | 2024 | Explainable {{Agency | State-of-the-art reinforcement learning agents are capable of outperforming human experts at games like chess, Go, and S... |
| Explainable {{Reinforcement Learning | Milani, Stephanie, Topin, Nicholay, Veloso, Manuela et al. | 2024 | ACM Comput. Surv. | A novel taxonomy for organizing the XRL literature that prioritizes the RL setting is proposed, and three high-level categories are proposed: feature importance, learning process and Markov decision process, and policy-level. |
| Generative {{Design | Pettit, Jacob, Lee, Chak Shing, Yang, Jiachen et al. | 2024 | — | Decision trees are an attractive choice for modeling policies in control environments due to their interpretability, con... |
| Explainable {{Agency | 2024 | — | This book focuses on a subtopic of explainable AI (XAI) called explainable agency (EA), which involves producing records... | |
| Combining Data and Theory for Derivable Scientific Discovery with {{AI-Descartes | Cornelio, Cristina, Dash, Sanjeeb, Austel, Vernon et al. | 2023 | Nature Communications | A method to enable principled derivations of models of natural phenomena from axiomatic knowledge and experimental data by combining logical reasoning with symbolic regression is developed and demonstrated for Kepler's third law of planetary motion, Einstein's relativistic time-dilation law, and Langmuir's theory of adsorption. |
| Efficient {{Symbolic Policy Learning | Guo, Jiaming, Zhang, Rui, Peng, Shaohui et al. | 2023 | — | Deep reinforcement learning (DRL) has led to a wide range of advances in sequential decision-making tasks. However, the ... |
Meta-learning approaches for fast adaptation in changing control environments.
| Title | Authors | Year | Venue | TL;DR |
|---|---|---|---|---|
| A {{Hybrid Meta-Learning Framework | Guo, Rui, Li, Yang, Cao, Xiuqing et al. | 2026 | Verification, {{Model Checking | Safety-critical cyber physical systems usually require controllers that not only meet performance goal but also guarante... |
| {{AI-Driven Control Strategies | Lalitha, S D, Danamaraju, Chaitanya, Raj, M. Prabhu et al. | 2024 | 2024 {{Ninth International Conference | This research examines power electronics converter AI-driven control techniques in detail and offers new opportunity to construct smart, adaptive, and high-performing energy conversion systems. |
| Hierarchical {{Meta-learning-based Adaptive Controller | Xie, Fengze, Shi, Guanya, O’Connell, Michael et al. | 2024 | 2024 {{IEEE International Conference | We study how to design learning-based adaptive controllers that enable fast and accurate online adaptation in changing e... |
Model-based and data-driven methods for DC-DC converters, inverters, and grid-connected systems.
| Title | Authors | Year | Venue | TL;DR |
|---|---|---|---|---|
| (13) {{Power Electronics Meets GenAI | 2025 | — | — | |
| Enhancing {{Power Converters Design | 2025 | — | — | |
| Python and {{SIMETRIX | Ansari, Danish Kaleem, Giovanni, Capodivacca, Amin, Syed Usman et al. | 2024 | 2024 39th {{Conference | In this paper, an advanced framework for the optimizer of DC-DC buck converters, which utilizes Python for algorithm imp... |
| Online Switching Control with Stability and Regret Guarantees | Li, Yingying, Preiss, James A., Li, Na et al. | 2023 | Proceedings of {{The | This paper considers online switching control with a finite candidate controller pool, an unknown dynamical system, and ... |
| The Optimal Control of Power Electronic Embedded Networks in {{More Electric Aircraft | Dewar, David N. | 2021 | — | With the advancement of power electronic technologies over recent decades, there has been an overall increase in the uti... |
| Model-Based Synthesis of Control for Power Electronic Converters | Gilev, Bogdan, Hinov, Nikolay | 2021 | AIP Conference Proceedings | The paper discusses a methodology for model-based synthesis of power electron converter control using the MATLAB/ Simuli... |
| 面向直流供电的电力电子变换器切换面控制及数字化实现 | 李景灏 | 2020 | — | 近年来,基于电力电子变换器的直流供电技术获得了越来越多的应用,直流供电的电能质量问题也日益凸显。为了满足直流供电系统的电能质量要求,有必要考虑优化电力电子变换器的控制策略以提高系统动态性能。切换面控制是一类非线性控制策略。与传统线性控制相比... |
| 电力电子系统的无源性和抗干扰控制理论与应用研究 | 贺伟 | 2018 | — | citation: 13 |
AI-driven scientific discovery tools that underpin the next generation of automated engineering.
| Title | Authors | Year | Venue | TL;DR |
|---|---|---|---|---|
| An {{AI | Aygün, Eser, Belyaeva, Anastasiya, Comanici, Gheorghe et al. | 2025 | — | An AI system that creates expert-level scientific software whose goal is to maximize a quality metric is presented, which generated state-of-the-art software for geospatial analysis, neural activity prediction in zebrafish, time series forecasting and numerical solution of integrals. |
| Towards {{Scientific Discovery | Reddy, Chandan K., Shojaee, Parshin | 2025 | Proceedings of the AAAI Conference on Artificial I | Scientific discovery is a complex cognitive process that has driven human knowledge and technological progress for centu... |
| Title | Authors | Year | Venue | TL;DR |
|---|---|---|---|---|
| Automating {{Automation | 2026 | — | Provides society information that may include news, reviews or technical notes that should be of interest to practitione... | |
| Meta-{{Control | 2026 | — | — | |
| Evolutionary Optimization of Model Merging Recipes | Akiba, Takuya, Shing, Makoto, Tang, Yujin et al. | 2025 | Nature Machine Intelligence | An evolutionary approach that automatically discovers effective combinations of diverse open-source models, harnessing their collective intelligence without requiring extensive additional training data or compute is proposed, allowing optimization beyond just the weights of the individual models. |
| Challenges and Opportunities in the Industrial Usage Controller Synthesis Tools: {{A | Banerjee, Amar, Choppella, Venkatesh | 2025 | Results in Control and Optimization | Controller synthesis is pivotal in automating control system design from formal specifications and enhancing industrial ... |
| A Large Language Model for Advanced Power Dispatch | Cheng, Yuheng, Zhao, Huan, Zhou, Xiyuan et al. | 2025 | Scientific Reports | A novel dataset construction technique is developed that harnesses various data sources to fine-tune GAIA for optimal performance in power dispatch operations, and streamlines LLM training, allowing for the seamless integration of multidimensional data in power system management. |
| From {{Mathematical Reasoning | Chen, Zhengyu, Wang, Yudong, Xiao, Teng et al. | 2025 | — | This study analyzes PRMs from multiple perspectives, including training methodologies, scalability, and generalization capabilities, and indicates that PRMs trained on mathematical datasets exhibit performance comparable to those tailored for code generation, suggesting robust cross-domain generalization. |
| Leveraging {{Predictions | Cui, Wenqi, Xie, Yiheng, Low, Steven et al. | 2025 | — | High variability of solar PV and sudden changes in load (e.g., electric vehicles and storage) can lead to large voltage ... |
| Mathematics with Large Language Models as Provers and Verifiers | Duc, Hieu Le, Liberti, Leo | 2025 | — | During 2024 and 2025 the discussion about the theorem-proving capabilities of large language models started reporting in... |
| {{LILAD | Jena, Amit, Li, Na, Xie, Le | 2025 | — | System identification in control theory aims to approximate dynamical systems from trajectory data. While neural network... |
| Machine Learning Solutions Looking for {{PDE | 2025 | Nature Machine Intelligence | Machine learning models are promising approaches to tackle partial differential equations, which are foundational descri... | |
| {{AuDeRe | Meng, Yue, Chen, Fei, Chen, Yongchao et al. | 2025 | — | Recent advancements in large language models (LLMs) have shown significant promise in various domains, especially roboti... |
| A {{New Embedded Control Architecture | 2025 | — | — | |
| {{OpenAI ChatGPT | 2025 | — | — | |
| Optimization and {{Control | 2025 | — | — | |
| Durvalumab {{With | Peters, Solange, Cho, Byoung Chul, Luft, Alexander V. et al. | 2025 | Journal of Thoracic Oncology | Introduction: The primary analysis (median follow-up 34.9 mo across all arms) of the phase 3 POSEIDON study revealed a s... |
| About {{PSC | 2025 | — | — | |
| {{SVG | 2025 | — | — | |
| Data-Driven Control Methods of Dual-Active-Bridge-Based Grid-Connected Battery Energy Storage System | Zeng, Yu | 2025 | — | Carbon dioxide emissions cause global warming and a series of environmental problems. To avoid the worst effects of clim... |
| Decision {{Transformer | Zhang, Xiangyuan, Mao, Weichao, Qiu, Haoran et al. | 2025 | 2025 {{American Control Conference | Closed-loop control of nonlinear dynamical systems with partial-state observability demands expert knowledge of a divers... |
| Learning from Models beyond Fine-Tuning | Zheng, Hongling, Shen, Li, Tang, Anke et al. | 2025 | Nature Machine Intelligence | A comprehensive review of the current methods based on FM from the perspective of LFM, in order to help readers better understand the current research status and ideas is given. |
| Data {{Modeling | Zuo, Xiangwu | 2025 | — | This dissertation investigates approaches in data handling within the domain of Artificial Intelligence (AI), covering d... |
| {{GENERATIVE AI | Chowdhury, Abdullahi, Chowdhury, Adyan, Hoque, Nabila et al. | 2024 | — | — |
| {{LLM4PLC | Fakih, Mohamad, Dharmaji, Rahul, Moghaddas, Yasamin et al. | 2024 | — | This work proposes LLM4PLC, a user-guided iterative pipeline leveraging user feed-back and external verification tools - including grammar checkers, compilers and SMV verifiers - to guide the LLM's generation to enhance the generation potential of LLM. |
| {{AI-driven | He, Yang-Hui | 2024 | Nature Reviews Physics | It is argued that while the theorist is in no way in danger of being replaced by AI in the near future, the hybrid of human expertise and AI algorithms will become an integral part of theoretical discovery. |
| Towards {{General Algorithm Discovery | Kuang, Yufei, Wang, Jie, Zhou, Yuyan et al. | 2024 | — | Machine learning (ML) approaches have been successfully applied to accelerating exact combinatorial optimization (CO) so... |
| Online {{Policy Optimization | Lin, Yiheng, Preiss, James A., Xie, Fengze et al. | 2024 | Proceedings of {{Thirty Seventh Conference | We study online policy optimization in nonlinear time-varying systems where the true dynamical models are unknown to the... |
| Meta-{{Control | Wei, Tianhao, Ma, Liqian, Chen, Rui et al. | 2024 | — | The requirements for real-world manipulation tasks are diverse and often conflicting; some tasks require precise motion ... |
| Stepwise {{Self-Consistent Mathematical Reasoning | Zhao, Zilong, Rong, Yao, Guo, Dongyang et al. | 2024 | — | This work introduces a novel algorithm, namely Stepwise Self-Consistent Chain-of-Thought (SSC-CoT), which employs a strategy of selecting intermediate steps based on the intersection of various reasoning chains and enables the model to discover critical intermediate steps by querying a knowledge graph comprising relevant domain knowledge. |
| Code as {{Policies | Liang, Jacky, Huang, Wenlong, Xia, Fei et al. | 2023 | — | Large language models (LLMs) trained on code completion have been shown to be capable of synthesizing simple Python prog... |
| Electrifying {{Discoveries | Patero, Judelyn | 2023 | International Journal of Advanced Research in Scie | This research investigates a novel educational paradigm merging interactive technology with conventional pedagogy to enh... |
| Meta-{{Learning-Based Optimal Control | Tang, Zhiqiang, Wang, Peiyi, Xin, Wenci et al. | 2023 | 2023 {{IEEE International Conference | Safe and efficient robot-environment interaction is a critical but challenging problem as robots are being increasingly ... |
| Meta-Control of the Exploration-Exploitation Dilemma Emerges from Probabilistic Inference over a Hierarchy of Time Scales | Marković, Dimitrije, Goschke, Thomas, Kiebel, Stefan J. | 2021 | Cognitive, Affective & Behavioral Neuroscience | Cognitive control is typically understood as a set of mechanisms that enable humans to reach goals that require integrat... |
| 电力电子变换器系统先进控制理论及其应用研究 | 王佐 | 2020 | — | citation: 3 |
| Policy {{Distillation | Rusu, Andrei A., Colmenarejo, Sergio Gomez, Gulcehre, Caglar et al. | 2016 | — | Policies for complex visual tasks have been successfully learned with deep reinforcement learning, using an approach cal... |
| Introduction to {{Physical Modeling | Tiller, Michael | 2001 | — | 3. 8 Problems . . . 66 4 ENABLING REUSE 69 4. 1 Concepts . . . . . . . . 69 4. 2 Exploiting commonality 70 4. 3 Reusable... |
| Large {{Language Models | Dharmaji, Rahul | — | — | — |
| {{FAST | Pertsch, Karl, Stachowicz, Kyle, Ichter, Brian et al. | — | — | Autoregressive sequence models, such as Transformer-based vision-language action (VLA) policies, can be tremendously eff... |
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