MCCoder: Streamlining Motion Control with LLM-Assisted Code Generation and Rigorous Verification

Large Language Models (LLMs) have shown considerable promise in code generation. However, the automation sector, especially in motion control, continues to rely heavily on manual programming due to the complexity of tasks and critical safety considerations. In this domain, incorrect code execution can pose risks to both machinery and personnel, necessitating specialized expertise. To address these challenges, we introduce MCCoder, an LLM-powered system designed to generate code that addresses complex motion control tasks, with integrated soft-motion data verification. MCCoder enhances code generation through multitask decomposition, hybrid retrieval-augmented generation (RAG), and self-correction with a private motion library. Moreover, it supports data verification by logging detailed trajectory data and providing simulations and plots, allowing users to assess the accuracy of the generated code and bolstering confidence in LLM-based programming. To ensure robust validation, we propose MCEVAL, an evaluation dataset with metrics tailored to motion control tasks of varying difficulties. Experiments indicate that MCCoder improves performance by 11.61% overall and by 66.12% on complex tasks in MCEVAL dataset compared with base models with naive RAG. This system and dataset aim to facilitate the application of code generation in automation settings with strict safety requirements. MCCoder is publicly available at https://github.com/MCCodeAI/MCCoder.

A Domain-adaptive Physics-informed Neural Network for Inverse Problems of Maxwell's Equations in Heterogeneous Media

Maxwell's equations are a collection of coupled partial differential equations (PDEs) that, together with the Lorentz force law, constitute the basis of classical electromagnetism and electric circuits. Effectively solving Maxwell's equations is crucial in various fields, like electromagnetic scattering and antenna design optimization. Physics-informed neural networks (PINNs) have shown powerful ability in solving PDEs. However, PINNs still struggle to solve Maxwell's equations in heterogeneous media. To this end, we propose a domain-adaptive PINN (da-PINN) to solve inverse problems of Maxwell's equations in heterogeneous media. First, we propose a location parameter of media interface to decompose the whole domain into several sub-domains. Furthermore, the electromagnetic interface conditions are incorporated into a loss function to improve the prediction performance near the interface. Then, we propose a domain-adaptive training strategy for da-PINN. Finally, the effectiveness of da-PINN is verified with two case studies.