Related papers: Test samples and infrastructure for accelerator ma…
Neutronic calculations for reactors are a daunting task when using Monte Carlo (MC) methods. As high-performance computing has advanced, the simulation of a reactor is nowadays more readily done, but design and optimization with multiple…
Knowledge of the fundamental limitations on a magnetic trap for neutral particles is of paramount interest to designers as it allows for the rapid assessment of the feasibility of specific trap requirements or the quality of a given design.…
Recent developments concerning localized-magnon eigenstates in strongly frustrated spin lattices and their effect on the low-temperature physics of these systems in high magnetic fields are reviewed. After illustrating the construction and…
The swept-field experiments on magnetic molecular solids such as \Fe8 are studied using Monte Carlo simulations. A kinetic equation is developed to understand the phenomenon. It is found that the simulations provide a quantitatively…
It is shown that a single molecular magnet placed in a rapidly oscillating magnetic field displays the phenomenon of quenching of tunneling processes. The results open a way to manipulate the quantum states of molecular magnets by means of…
There should be no question that magnetism and superconductivity appear in close proximity in many if not most of the unconventional superconductors. These two phases are importantly correlated: the strongest manifestations of this…
Learning with limited data is one of the biggest problems of machine learning. Current approaches to this issue consist in learning general representations from huge amounts of data before fine-tuning the model on a small dataset of…
The low-beta magnet systems are located in the Large Hadron Collider (LHC) insertion regions around the four interaction points. They are the key elements in the beams focusing/defocusing process allowing proton collisions at luminosity up…
Many applications of imitation learning require the agent to generate the full distribution of behaviour observed in the training data. For example, to evaluate the safety of autonomous vehicles in simulation, accurate and diverse behaviour…
The discovery of ambient superconductivity would mark an epochal breakthrough long-awaited for over a century, potentially ushering in unprecedented scientific and technological advancements. The recent findings on high-temperature…
Reliable powering of accelerator magnets requires reliable power converters and controls, able to meet the powering specifications in the long term. In this paper, some of the issues that will challenge a power converter controls engineer…
Machine learned chemical potentials have shown great promise as alternatives to conventional computational chemistry methods to represent the potential energy of a given atomic or molecular system as a function of its geometry. However,…
In Networked Control Systems (NCS), the absence of physical communication links in the loop leads to relevant issues, such as measurement delays and asynchronous execution of the control commands. These issues may lead to unwanted control…
The use of learned dynamics models, also known as world models, can improve the sample efficiency of reinforcement learning. Recent work suggests that the underlying causal graphs of such dynamics models are sparsely connected, with each of…
At the LHC, an effective theory of the Higgs sector allows us to analyze kinematic distributions in addition to inclusive rates, although there is no clear hierarchy of scales. We systematically analyze how well dimension-6 operators…
Single-domain ferromagnetic nanoparticle systems can be used to transfer energy from a time-dependent magnetic field into their environment. This local heat generation, i.e., magnetic hyperthermia, receives applications in cancer therapy…
Specific heat studies of CeRhIn5 as functions of pressure and magnetic field have been used to explore the relationship between magnetism and unconventional superconductivity, both of which involve the 4f electron of Ce. Results of these…
This paper addresses the problem of classifying observations when features are context-sensitive, specifically when the testing set involves a context that is different from the training set. The paper begins with a precise definition of…
Deep learning has triggered the current rise of artificial intelligence and is the workhorse of today's machine intelligence. Numerous success stories have rapidly spread all over science, industry and society, but its limitations have only…
We review the studies on the emergent phases of superconductvity and magnetism in the $f$-electron derived heavy-fermion (HF) systems by means of the nuclear-quadrupole-resonance (NQR) under pressure. These studies have unraveled a rich…