Related papers: Model Driven Ramp Control at RHIC
Model-reference adaptive systems refer to a consortium of techniques that guide plants to track desired reference trajectories. Approaches based on theories like Lyapunov, sliding surfaces, and backstepping are typically employed to advise…
In preparation for operation of multiple Rare Isotope Beams (RIBs) when the Advanced Rare Isotope Laboratory (ARIEL) becomes operational, TRIUMF embarked on a program of advanced beam tuning applications and machine learning tools. The…
An Effective String Rope Model (ESRM) for heavy ion collisions at RHIC energies is presented. Our model takes into account baryon recoil for both target and projectile, arising from the acceleration of partons in an effective field,…
A plastic scintillator paddle detector with embedded fiber light guides and photomultiplier tube readout, referred to as the Reaction Plane Detector (RXNP), was designed and installed in the PHENIX experiment prior to the 2007 run of the…
The integrated HydroKinetic Model (iHKM) is applied to analyse the results of direct photon spectra and elliptic flow measurements in 200A GeV Au+Au collisions at RHIC for the three centrality bins. We detect the strong centrality…
The Relativistic Heavy Ion Collider (RHIC) at Brookhaven National Laboratory is the first accelerator facility that can accelerate, store and collide spin polarized proton beams. This development enables a physics program aimed at…
Model-based reinforcement learning attempts to use an available or learned model to improve the data efficiency of reinforcement learning. This work proposes a one-step lookback approach that jointly learns the deep incremental model and…
Targeted drug delivery in the gastrointestinal (GI) tract using magnetic robots offers a promising alternative to systemic treatments. However, controlling these robots is a major challenge. Stationary magnetic systems have a limited…
Trapped atomic ions are among the most advanced platforms for quantum simulation, computation, and metrology, offering long coherence times and precise, individual control over both internal and motional degrees of freedom. In this review,…
Based on A Multi-Phase Transport model, the elliptic flow $v_{2}$ of $\phi$ mesons which is reconstructed from $K^{+}K^{-}$ at the Relativistic Heavy Ion Collider (RHIC) energy has been studied. The results show that reconstructed $v_{2}$…
The ramp-reversal memory (RRM) effect in metal-insulator transition metal oxides (TMOs), a non-volatile resistance change induced by repeated temperature cycling, has attracted considerable interest in neuromorphic computing and…
Robust adaptive model predictive control (RAMPC) is a novel control method that combines robustness guarantees with respect to unknown parameters and bounded disturbances into a model predictive control scheme. However, RAMPC has so far…
We consider the problem of designing an algorithm to allow a car to autonomously merge on to a highway from an on-ramp. Two broad classes of techniques have been proposed to solve motion planning problems in autonomous driving: Model…
Multiplexed operations and extended coherent control over multiple trapping sites are fundamental requirements for a trapped-ion processor in a large scale architecture. Here we demonstrate these building blocks using a surface-electrode…
We simulate the dynamics of Au+Au collisions at the Relativistic Heavy Ion Collider (RHIC) with a hybrid model that treats the quark-gluon plasma macroscopically as an ideal fluid, but models the hadron resonance gas microscopically using a…
Deep reinforcement learning (RL) has made it possible to solve complex robotics problems using neural networks as function approximators. However, the policies trained on stationary environments suffer in terms of generalization when…
A model for energy, pressure and flow velocity distributions at the beginning of ultra-relativistic heavy ion collisions is presented, which can be used as an initial condition for hydrodynamic calculations. Our model takes into account…
We introduce Reactive Action and Motion Planner (RAMP), which combines the strengths of sampling-based and reactive approaches for motion planning. In essence, RAMP is a hierarchical approach where a novel variant of a Model Predictive Path…
Over the years, complex control approaches have been developed to control the motion of a bicycle. Reinforcement Learning (RL), a branch of machine learning, promises easy deployment of so-called agents. Deployed agents are increasingly…
We present fully dynamic simulations of heavy ion collisions at RHIC energies within the perturbative QCD-based partonic transport model BAMPS, focusing on the simultaneous investigation of jet-quenching and elliptic flow. The model…