This article explores the application of reinforcement learning (RL) to Partial Differential Equations (PDEs), highlighting the complexity and challenges involved in controlling systems described by PDEs compared to Ordinary Differential Equations (ODEs). It discusses various approaches, including genetic programming and neural network-based methods, and presents experimental results on controlling PDE systems like the diffusion equation and Kuramoto–Sivashinsky equation. The author emphasizes the potential of machine learning to improve understanding and control of PDE systems, which have wide-ranging applications in fields like fluid dynamics, thermodynamics, and engineering.
MIT researchers have developed a method using large language models to detect anomalies in complex systems without the need for training. The approach, called SigLLM, converts time-series data into text-based inputs for the language model to process. Two anomaly detection approaches, Prompter and Detector, were developed and showed promising results in initial tests.
