6 papers
RAISE: LLM-based Automated Heuristic Design with Robust Adversary Instance Search
Fei Liu, Alessio Figalli, Patrick Owen +1
Automated Heuristic Design (AHD) with Large Language Models (LLMs) has shown remarkable progress in discovering high-quality heuristics. However, existing LLM-based AHD methods opt…
On the Codesign of Scientific Experiments and Industrial Systems
Tommaso Dorigo, Pietro Vischia, Shahzaib Abbas +84
The optimization of large experiments in fundamental science, such as detectors for subnuclear physics at particle colliders, shares with the optimization of complex systems for in…
Towards replacing detector simulation with heterogeneous GNNs in flavour physics analyses
Guillermo Hijano, Davide Lancierini, Alexander Mclean Marshall +8
Driven by the increasing volume of recorded data, the demand for simulation from experiments based at the Large Hadron Collider will rise sharply in the coming years. Addressing th…
Large Language Models for Physics Instrument Design
Sara Zoccheddu, Shah Rukh Qasim, Patrick Owen +1
We study the use of large language models (LLMs) for physics instrument design and compare their performance to reinforcement learning (RL). Using only prompting, LLMs are given ta…
Ultra-Fast Muon Transport via Histogram Sampling on GPUs
Luis Felipe P. Cattelan, Shah Rukh Qasim, Patrick H. Owen +1
We present a GPU-accelerated method for muon transport based on histogram sampling that delivers orders of magnitude faster performance than CPU-based Geant4 simulation. Our method…
Physics Instrument Design with Reinforcement Learning
Shah Rukh Qasim, Patrick Owen, Nicola Serra
We present a case for the use of Reinforcement Learning (RL) for the design of physics instrument as an alternative to gradient-based instrument-optimization methods. It's applicab…