AI Control Scientist (AICS) is a large language model–driven agent that automatically generates optimized controllers from language design requirements. It comprises a Task Modeling Agent that translates user needs into engineering constraints, a Controller Design Agent that produces candidate controller structures and code, and a Parameter Tuning Agent that refines parameters to meet closed‑loop performance criteria. Experiments show AICS outperforms existing automated baselines in design success rate and optimization efficiency, enabling the creation of multiple representative control systems.
By Haiteng Wang, Weihao Li, Jing Zhang, Lei Ren
arXiv:2607. 26594v1 Announce Type: cross Abstract: PID tuning for chemical processes commonly relies on identified process models, whereas plant engineers often retune loops iteratively by observing responses, diagnosing deficiencies, adjusting gains, and validating the result.
By Zhoupeng Shou, Xiaodong Hong, Congjing Ren, Jingdai Wang, Yongrong Yang, Zuwei Liao
arXiv:2608. 11220v1 Announce Type: new Abstract: Nowadays, the creation of a process flow diagram (PFD) and its subsequent transformation into a piping and instrumentation diagram (P&ID) is predominantly performed manually.
By Timur Zakarin, Sergei Voitov, Sergei Shumilin, Evgeny Burnaev
The paper introduces AI Control Scientist (AICS), a large language model–driven agent that automatically generates optimized controllers from language design requirements. AICS consists of a Task Modeling Agent that translates user needs into engineering constraints, a Controller Design Agent that produces candidate controller structures and code, and a Parameter Tuning Agent that refines parameters to meet closed‑loop performance criteria. Experiments show that AICS outperforms existing automated baselines in design success rate and optimization efficiency, demonstrating its potential to shift control system design from human‑driven to agent‑driven approaches.
The paper introduces little m, an AI agent that helps formulate industrial process control models by combining a domain-specific knowledge repository with LLM-driven interaction. It tackles the challenge of converting messy real-world specifications, including natural language and spatial diagrams, into rigorous mathematical optimization models. The authors also present IPC-Bench, a multimodal dataset of 50 canonical scenarios, and show through automated and human evaluations that little m outperforms state‑of‑the‑art LLMs in generating semantically correct models.
By Yongchao Ye, Xinyu He, Dutliff Boshoff, Way Kuo, Lishuai Li
arXiv:2607. 09713v1 Announce Type: new Abstract: A key step toward autonomous industrial operation is the ability to create and reconfigure control policies from natural-language requirement specifications, with minimal or no manual redesign.
By Yuchen Wang, Javal Vyas, Tong Liu, Mehmet Mercangoz
arXiv:2606. 14350v1 Announce Type: cross Abstract: Artificial Intelligence (AI) systems must typically satisfy service-level objectives including accuracy, latency, and cost.
By Milos Gravara, Andrija Stanisic, Stefan Nastic
arXiv:2606. 04246v1 Announce Type: new Abstract: Automatic generation of RTL code for digital hardware designs remains challenging due to long-horizon reasoning, multi-step dependencies, and strict correctness constraints in Verilog and VHDL.
By Prashanth Vijayaraghavan, Apoorva Nitsure, Luyao Shi, Ehsan Degan, Vandana Mukherjee
arXiv:2608. 03600v1 Announce Type: new Abstract: Partial differential equations (PDEs) become actionable in science and engineering not as isolated formulae, but as executable workflows that connect modelling assumptions, governing equations, numerical solvers, diagnostics, and decisions.
By Han Wan, Rui Zhang, Hao Sun
arXiv:2607. 22759v1 Announce Type: cross Abstract: Large language models (LLMs) show promise in code generation, but their capabilities to produce correct, synthesizable hardware description language (HDL) code still remain to be properly benchmarked.
By Angshuman Chakravertty, Rahul Koshti, Buddhi Prakash Sharma, Vinay Chamola
arXiv:2609.17440v1 Announce Type: new
Abstract: Optimizing industrial process flowsheets is often computationally prohibitive due to the high cost of rigorous simulations and the curse of dimensional...
By Niki Triantafyllou, Andrea Bernardi, Maria M. Papathanasiou
arXiv:2503.18460v2 Announce Type: replace-cross
Abstract: Modelica is a widely adopted language for simulating complex physical systems, yet effective model creation and optimization require substant...
By Jiahui Xiang, Tong Ye, Peiyu Liu, Yinan Zhang, Wenhai Wang