Search Results for author:
Found 9 papers, 5 papers with code
Large Language Models for Human-Machine Collaborative Particle Accelerator Tuning through Natural Language
Ultimately, this work represents yet another complex task that LLMs are capable of solving and promises to help accelerate the deployment of autonomous tuning algorithms to the day-to-day operations of particle accelerators.
Cheetah: Bridging the Gap Between Machine Learning and Particle Accelerator Physics with High-Speed, Differentiable Simulations
Machine learning has emerged as a powerful solution to the modern challenges in accelerator physics.
Safe Multi-Task Bayesian Optimization
For further speed-up reduced physical models of the system can be incorporated into the optimization to accelerate the process, since the models are able to offer an approximation of the actual system, and sampling from them is significantly cheaper.
PACuna: Automated Fine-Tuning of Language Models for Particle Accelerators
Navigating the landscape of particle accelerators has become increasingly challenging with recent surges in contributions.
Textual Analysis of ICALEPCS and IPAC Conference Proceedings: Revealing Research Trends, Topics, and Collaborations for Future Insights and Advanced Search
In this paper, we show a textual analysis of past ICALEPCS and IPAC conference proceedings to gain insights into the research trends and topics discussed in the field.
Learning to Do or Learning While Doing: Reinforcement Learning and Bayesian Optimisation for Online Continuous Tuning
Online tuning of real-world plants is a complex optimisation problem that continues to require manual intervention by experienced human operators.
Structured IQC Synthesis of Robust $\mathcal{H}_2$ Controllers in the Frequency Domain
The problem of robust controller synthesis for plants affected by structured uncertainty, captured by integral quadratic constraints, is discussed.
Online Computation of Terminal Ingredients in Distributed Model Predictive Control for Reference Tracking
A distributed model predictive control scheme is developed for tracking piecewise constant references where the terminal set is reconfigured online, whereas the terminal controller is computed offline.
Distributed Model Predictive Control with Reconfigurable Terminal Ingredients for Reference Tracking
The proposed scheme is tested in simulation where the proposed MPC problem is solved using distributed optimization.
