Related papers: Pareto Optimization of a Laser Wakefield Accelerat…
Laser wakefield accelerator experiments have made enormous progress over the past $\sim 20$ years, but their promise to revolutionize high-energy particle sources is only beginning to be realized. To make the next step toward engineering…
This work considers a multiobjective version of the unit commitment problem that deals with finding the optimal generation schedule of a firm, over a period of time and a given electrical network. With growing importance of environmental…
Plasma wakefield accelerators driven by particle beams are capable of providing accelerating gradient several orders of magnitude higher than currently used radio-frequency technology, which could reduce the length of particle accelerators,…
Laser-wakefield acceleration is a promising technique for the next generation of ultra-compact, high-energy particle accelerators. However, for a meaningful use of laser-driven particle beams it is necessary that they present a high degree…
Particle accelerators are invaluable discovery engines in the chemical, biological and physical sciences. Characterization of the accelerated beam response to accelerator input parameters is of-ten the first step when conducting…
Expensive multi-objective optimization problems can be found in many real-world applications, where their objective function evaluations involve expensive computations or physical experiments. It is desirable to obtain an approximate Pareto…
Beam-driven plasma wakefield accelerators typically use the external injection scheme to ensure controllable beam quality at injection. However, the externally injected witness bunch may exhibit a non-Gaussian transverse density…
This work proposes a methodology to find performance and energy trade-offs for parallel applications running on Heterogeneous Multi-Processing systems with a single instruction-set architecture. These offer flexibility in the form of…
Multi-task learning solves multiple correlated tasks. However, conflicts may exist between them. In such circumstances, a single solution can rarely optimize all the tasks, leading to performance trade-offs. To arrive at a set of optimized…
There are a lot of real-world black-box optimization problems that need to optimize multiple criteria simultaneously. However, in a multi-objective optimization (MOO) problem, identifying the whole Pareto front requires the prohibitive…
Positron acceleration in plasma wakefield faces significant challenges since the positron beam must be pre-generated and precisely coupled into the wakefield, and most critically, suffers from defocusing issues. Here we propose a scheme…
Optimising black-box functions is important in many disciplines, such as tuning machine learning models, robotics, finance and mining exploration. Bayesian optimisation is a state-of-the-art technique for the global optimisation of…
Bayesian optimization is a methodology to optimize black-box functions. Traditionally, it focuses on the setting where you can arbitrarily query the search space. However, many real-life problems do not offer this flexibility; in…
In this work, we study Bayesian quantum parameter estimation given a finite number of uses of the process encoding one or more unknown physical quantities. For multiple uses, it is conventional to classify quantum metrological protocols as…
Inferring viscoelasticity parameters is a key challenge that often leads to non-unique solutions when fitting rheological data. In this context, we propose a machine learning approach that utilizes Bayesian optimization for parameter…
Accelerated discovery in materials science demands autonomous systems capable of dynamically formulating and solving design problems. In this work, we introduce a novel framework that leverages Bayesian optimization over a problem…
Plasma Wakefield Acceleration represents one of the most promising techniques able to overcome the limits of conventional RF technology and make possible the development of compact accelerators. With respect to the laser-driven schemes, the…
Multiobjective combinatorial optimization (MOCO) problems can be found in many real-world applications. However, exactly solving these problems would be very challenging, particularly when they are NP-hard. Many handcrafted heuristic…
Electronic materials exhibiting phase transitions between metastable states (e.g., metal-insulator transition materials with abrupt electrical resistivity transformations) are challenging to decode. For these materials, conventional machine…
The development of ultra-intense laser-based sources of high energy ions is an important goal, with a variety of potential applications. One of the barriers to achieving this goal is the need to maximize the conversion efficiency from laser…