Cheetah: Bridging the Gap Between Machine Learning and Particle Accelerator Physics with High-Speed, Differentiable Simulations

• URL: http://arxiv.org/abs/2401.05815v1
• Date: Thu, 11 Jan 2024 10:30:40 GMT
• Title: Cheetah: Bridging the Gap Between Machine Learning and Particle Accelerator Physics with High-Speed, Differentiable Simulations
• Authors: Jan Kaiser, Chenran Xu, Annika Eichler, Andrea Santamaria Garcia
• Abstract summary: We introduce Cheetah, a PyTorch-based high-speed differentiable linear-beam dynamics code. Cheetah enables the fast collection of large data sets by reducing times by multiple orders of magnitude. We showcase the utility of Cheetah through five examples, including reinforcement learning, gradient-based beamline tuning, gradient-based system identification, physics-informed optimisation priors, and modular neural network surrogate modelling of space charge effects.
• Score: 2.7309692684728617
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning has emerged as a powerful solution to the modern challenges in accelerator physics. However, the limited availability of beam time, the computational cost of simulations, and the high-dimensionality of optimisation problems pose significant challenges in generating the required data for training state-of-the-art machine learning models. In this work, we introduce Cheetah, a PyTorch-based high-speed differentiable linear-beam dynamics code. Cheetah enables the fast collection of large data sets by reducing computation times by multiple orders of magnitude and facilitates efficient gradient-based optimisation for accelerator tuning and system identification. This positions Cheetah as a user-friendly, readily extensible tool that integrates seamlessly with widely adopted machine learning tools. We showcase the utility of Cheetah through five examples, including reinforcement learning training, gradient-based beamline tuning, gradient-based system identification, physics-informed Bayesian optimisation priors, and modular neural network surrogate modelling of space charge effects. The use of such a high-speed differentiable simulation code will simplify the development of machine learning-based methods for particle accelerators and fast-track their integration into everyday operations of accelerator facilities.

Related papers

• Open-Source High-Speed Flight Surrogate Modeling Framework [0.0]
  High-speed flight vehicles, which travel much faster than the speed of sound, are crucial for national defense and space exploration. accurately predicting their behavior under numerous, varied flight conditions is a challenge and often expensive. The proposed approach involves creating smarter, more efficient machine learning models.
  arXiv  Detail & Related papers  (2024-11-06T01:34:06Z)
• An optically accelerated extreme learning machine using hot atomic vapors [0.0]
  We present a new design combining the strong and tunable nonlinear properties of a light beam propagating through a hot atomic vapor with an Extreme Learning Machine model. We numerically and experimentally demonstrate the enhancement of the training using such free-space nonlinear propagation on a MNIST image classification task.
  arXiv  Detail & Related papers  (2024-09-06T14:36:56Z)
• Gaussian Splatting to Real World Flight Navigation Transfer with Liquid Networks [93.38375271826202]
  We present a method to improve generalization and robustness to distribution shifts in sim-to-real visual quadrotor navigation tasks. We first build a simulator by integrating Gaussian splatting with quadrotor flight dynamics, and then, train robust navigation policies using Liquid neural networks. In this way, we obtain a full-stack imitation learning protocol that combines advances in 3D Gaussian splatting radiance field rendering, programming of expert demonstration training data, and the task understanding capabilities of Liquid networks.
  arXiv  Detail & Related papers  (2024-06-21T13:48:37Z)
• Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research [76.93956925360638]
  Waymax is a new data-driven simulator for autonomous driving in multi-agent scenes. It runs entirely on hardware accelerators such as TPUs/GPUs and supports in-graph simulation for training. We benchmark a suite of popular imitation and reinforcement learning algorithms with ablation studies on different design decisions.
  arXiv  Detail & Related papers  (2023-10-12T20:49:15Z)
• Continual learning autoencoder training for a particle-in-cell simulation via streaming [52.77024349608834]
  upcoming exascale era will provide a new generation of physics simulations with high resolution. These simulations will have a high resolution, which will impact the training of machine learning models since storing a high amount of simulation data on disk is nearly impossible. This work presents an approach that trains a neural network concurrently to a running simulation without data on a disk.
  arXiv  Detail & Related papers  (2022-11-09T09:55:14Z)
• On Fast Simulation of Dynamical System with Neural Vector Enhanced Numerical Solver [59.13397937903832]
  We introduce a deep learning-based corrector called Neural Vector (NeurVec) NeurVec can compensate for integration errors and enable larger time step sizes in simulations. Our experiments on a variety of complex dynamical system benchmarks demonstrate that NeurVec exhibits remarkable generalization capability.
  arXiv  Detail & Related papers  (2022-08-07T09:02:18Z)
• Efficient Differentiable Simulation of Articulated Bodies [89.64118042429287]
  We present a method for efficient differentiable simulation of articulated bodies. This enables integration of articulated body dynamics into deep learning frameworks. We show that reinforcement learning with articulated systems can be accelerated using gradients provided by our method.
  arXiv  Detail & Related papers  (2021-09-16T04:48:13Z)
• PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable Physics [89.81550748680245]
  We introduce a new differentiable physics benchmark called PasticineLab. In each task, the agent uses manipulators to deform the plasticine into the desired configuration. We evaluate several existing reinforcement learning (RL) methods and gradient-based methods on this benchmark.
  arXiv  Detail & Related papers  (2021-04-07T17:59:23Z)
• Fast Modeling and Understanding Fluid Dynamics Systems with Encoder-Decoder Networks [0.0]
  We show that an accurate deep-learning-based proxy model can be taught efficiently by a finite-volume-based simulator. Compared to traditional simulation, the proposed deep learning approach enables much faster forward computation. We quantify the sensitivity of the deep learning model to key physical parameters and hence demonstrate that the inversion problems can be solved with great acceleration.
  arXiv  Detail & Related papers  (2020-06-09T17:14:08Z)
• Learning hierarchical behavior and motion planning for autonomous driving [32.78069835190924]
  We introduce hierarchical behavior and motion planning (HBMP) to explicitly model the behavior in learning-based solution. We transform HBMP problem by integrating a classical sampling-based motion planner. In addition, we propose a sharable representation for input sensory data across simulation platforms and real-world environment.
  arXiv  Detail & Related papers  (2020-05-08T05:34:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.