Related papers: Pareto Optimization of a Laser Wakefield Accelerat…
A multi-condition multi-objective optimization method that can find Pareto front over a defined condition space is developed for the first time using deep reinforcement learning. Unlike the conventional methods which perform optimization at…
The extreme electric fields created in high-intensity laser-plasma interactions could generate energetic ions far more compactly than traditional accelerators. Despite this promise, laser-plasma accelerators have remained stagnant at…
The optimization of atomic structures plays a pivotal role in understanding and designing materials with desired properties. However, conventional computational methods often struggle with the formidable task of navigating the vast…
We consider a multi-objective optimization problem with objective functions that are expensive to evaluate. The decision maker (DM) has unknown preferences, and so the standard approach is to generate an approximation of the Pareto front…
Training machine learning models inherently involves a resource-intensive and noisy iterative learning procedure that allows epoch-wise monitoring of the model performance. However, the insights gained from the iterative learning procedure…
Much recent research has been conducted in the area of Bayesian learning, particularly with regard to the optimization of hyper-parameters via Gaussian process regression. The methodologies rely chiefly on the method of maximizing the…
We propose to use tightly focused lasers to generate high quality electron beams in laser wakefield accelerators. In this scheme, the expansion of the laser beam after the focal position enlarges the size of wakefield bubble, which reduces…
This paper addresses the problem of constrained multi-objective optimization over black-box objective functions with practitioner-specified preferences over the objectives when a large fraction of the input space is infeasible (i.e.,…
Focusing petawatt-level laser beams to a variety of spot sizes for different applications is expensive in cost, labor and space. In this paper, we propose a plasma lens to flexibly resize the laser beam by utilizing the laser self-focusing…
Electron ptychography provides new opportunities to resolve atomic structures with deep sub-angstrom spatial resolution and studying electron-beam sensitive materials with high dose efficiency. In practice, obtaining accurate ptychography…
It can be computationally advantageous to perform computer simulations in a Lorentz boosted frame for a certain class of systems. However, even if the computer model relies on a covariant set of equations, it has been pointed out that…
We present a novel method for aligning a laser ionized plasma source to a pair of ultra-relativistic electron beams that comprise a plasma wakefield accelerator (PWFA). We achieve alignment by analyzing the plasma afterglow light observed…
Bayesian optimization is a sequential method for minimizing objective functions that are expensive to evaluate and about which few assumptions can be made. By using all gathered data to train a Gaussian process model for the function and…
Biological processes such as DNA replication, RNA transcription, and protein translation show remarkable speed and accuracy in selecting the right substrate from pools of chemically identical molecules. This result is obtained by…
Pareto optimality is capable of striking the optimal trade-off amongst the diverse conflicting QoS requirements of routing in wireless multihop networks. However, this comes at the cost of increased complexity owing to searching through the…
Matched beam loading in laser wakefield acceleration (LWFA), characterizing the state of flattening of the acceleration electric field along the bunch, leads to the minimization of energy spread at high bunch charges. Here, we demonstrate…
Multi-objective combinatorial optimization seeks Pareto-optimal solutions over exponentially large discrete spaces, yet existing methods sacrifice generality, scalability, or theoretical guarantees. We reformulate it as an online learning…
Hyperparameters play a critical role in the performances of many machine learning methods. Determining their best settings or Hyperparameter Optimization (HPO) faces difficulties presented by the large number of hyperparameters as well as…
To date, the multi-objective optimization literature has mainly focused on conflicting objectives, studying the Pareto front, or requiring users to balance tradeoffs. Yet, in machine learning practice, there are many scenarios where such…
Plasma-based accelerators are compact and provide high gradients, yet their practical use has been limited by energy gain, stability, beam quality, and energy transfer efficiency. Here, we address several of these challenges simultaneously…