activity
20242026
collaborators

5 papers

cs.AI2026

Multi-Agent Collaboration for Automated Design Exploration on High Performance Computing Systems

Harshitha Menon, Charles F. Jekel, Kevin Korner +15

Today's scientific challenges, from climate modeling to Inertial Confinement Fusion design to novel material design, require exploring huge design spaces. In order to enable high-i…

physics.acc-ph2026

Fast chaos indicator from auto-differentiation for dynamic aperture optimization

Ji Qiang, Jinyu Wan, Allen Qiang +1

Automatic differentiation provides an efficient means of computing derivatives of complex functions with machine precision, thereby enabling differentiable simulation. In this work…

physics.comp-ph2025

A multi-language auto-differentiation module and its application to a parallel particle-in-cell code on distributed computers

Ji Qianga, Yue Hao, Allen Qiang +1

The auto differentiable simulation is a type of simulation that outputs of the simulation include not only the simulation result itself, but also their derivatives with respect to…

physics.acc-ph2024

JuTrack: a Julia package for auto-differentiable accelerator modeling and particle tracking

Jinyu Wan, Helena Alamprese, Christian Ratcliff +2

Efficient accelerator modeling and particle tracking are key for the design and configuration of modern particle accelerators. In this work, we present JuTrack, a nested accelerato…

physics.acc-ph2024

Time-Delayed Koopman Network-Based Model Predictive Control for the FRIB RFQ

Jinyu Wan, Shen Zhao, Wei Chang +1

The radio-frequency quadrupole (RFQ) at the Facility for Rare Isotope Beams (FRIB) is a critical device to accelerate heavy ion beams from 12 keV/u to 0.5 MeV/u for state-of-the-ar…