Physics-informed Gaussian Process for Online Optimization of Particle Accelerators

by   Adi Hanuka, et al.

High-dimensional optimization is a critical challenge for operating large-scale scientific facilities. We apply a physics-informed Gaussian process (GP) optimizer to tune a complex system by conducting efficient global search. Typical GP models learn from past observations to make predictions, but this reduces their applicability to new systems where archive data is not available. Instead, here we use a fast approximate model from physics simulations to design the GP model. The GP is then employed to make inferences from sequential online observations in order to optimize the system. Simulation and experimental studies were carried out to demonstrate the method for online control of a storage ring. We show that the physics-informed GP outperforms current routinely used online optimizers in terms of convergence speed, and robustness on this task. The ability to inform the machine-learning model with physics may have wide applications in science.



There are no comments yet.


page 1

page 2

page 3

page 4


Online tuning and light source control using a physics-informed Gaussian process Adi

Operating large-scale scientific facilities often requires fast tuning a...

When Bifidelity Meets CoKriging: An Efficient Physics-Informed Multifidelity Method

In this work, we propose a framework that combines the approximation-the...

Physics-Informed CoKriging: A Gaussian-Process-Regression-Based Multifidelity Method for Data-Model Convergence

In this work, we propose a new Gaussian process regression (GPR)-based m...

An Application of Gaussian Process Modeling for High-order Accurate Adaptive Mesh Refinement Prolongation

We present a new polynomial-free prolongation scheme for Adaptive Mesh R...

Gaussian Process-based Min-norm Stabilizing Controller for Control-Affine Systems with Uncertain Input Effects

This paper presents a method to design a min-norm Control Lyapunov Funct...

Combined Global and Local Search for Optimization with Gaussian Process Models

Gaussian process (GP) model based optimization is widely applied in simu...

Machine learning based digital twin for dynamical systems with multiple time-scales

Digital twin technology has a huge potential for widespread applications...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.