Laser-plasma physics has developed rapidly over the past few decades as lasers have become both more powerful and more widely available. Early experimental and numerical research in this field was dominated by single-shot experiments with limited parameter exploration. However, recent technological improvements make it possible to gather data for hundreds or thousands of different settings in both experiments and simulations. This has sparked interest in using advanced techniques from mathematics, statistics and computer science to deal with, and benefit from, big data. At the same time, sophisticated modeling techniques also provide new ways for researchers to deal effectively with situation where still only sparse data are available. This paper aims to present an overview of relevant machine learning methods with focus on applicability to laser-plasma physics and its important sub-fields of laser-plasma acceleration and inertial confinement fusion.

Beam parameter optimization in accelerators involves multiple, sometimes competing objectives. Condensing these individual objectives into a single figure of merit unavoidably results in a bias towards particular outcomes, in absence of prior knowledge often in a non-desired way. Finding an optimal objective definition then requires operators to iterate over many possible objective weights and definitions, a process that can take many times longer than the optimization itself. A more versatile approach is multi-objective optimization, which establishes the trade-off curve or Pareto front between objectives. Here we present the first results on multi-objective Bayesian optimization of a simulated laser-plasma accelerator. We find that multi-objective optimization reaches comparable performance to its single-objective counterparts while allowing for instant evaluation of entirely new objectives. This dramatically reduces the time required to find appropriate objective definitions for new problems. Additionally, our multi-objective, multi-fidelity method reduces the time required for an optimization run by an order of magnitude. It does so by dynamically choosing simulation resolution and box size, requiring fewer slow and expensive simulations as it learns about the Pareto-optimal solutions from fast low-resolution runs. The techniques demonstrated in this paper can easily be translated into many different computational and experimental use cases beyond accelerator optimization.

贝叶斯优化已被证明是优化昂贵至尊评估系统的有效方法。然而，根据单一观察的成本，一个或多个目标的多维优化可能仍然是昂贵的。多保真优化通过包括多个更便宜的信息来源，例如数值模拟中的低分辨率近似来解决这个问题。用于多保真优化的采集功能通常基于勘探重算法，这些算法难以与多种目标的优化结合。在这里，我们认为预期的超越改善政策可以在许多情况下作为合适的替代品起作用。我们通过两步评估或在单个采集函数内纳入评估成本，额外的保真相关目标。这允许同时多目标和多保真优化，这允许以分数成本准确地建立帕累托集和前部。基准显示成本降低了一个数量级或更多的顺序。因此，我们的方法允许极其膨胀的黑盒功能进行静态优化。在现有的优化贝叶斯优化框架中实现了本方法简单且直接，可以立即扩展到批量优化。该技术还可用于组合不同的连续和/或离散保真度尺寸，这使得它们特别相关地与等离子体物理，流体动力学和许多科学计算分支中的模拟问题相关。

本地监督的学习旨在根据网络每个解耦模块的全局损耗函数的局部估计来训练神经网络。通常将辅助网络附加到模块上，以根据贪婪的本地损失近似梯度更新。尽管在平行性和减少记忆消耗方面是有利的，但这种训练的范式严重降低了神经网络的概括性能。在本文中，我们建议定期指导本地学习（PGL），该学习将全球客观重复地重复地重复纳入基于局部损坏的神经网络的培训，主要是增强模型的概括能力。我们表明，一个简单的周期性指导方案在记忆范围低的同时会带来显着的性能增长。我们在各种数据集和网络上进行了广泛的实验，以证明PGL的有效性，尤其是在具有许多解耦模块的配置中。

我们的方法从单个RGB-D观察中研究了以对象为中心的3D理解的复杂任务。由于这是一个不适的问题，因此现有的方法在3D形状和6D姿势和尺寸估计中都遭受了遮挡的复杂多对象方案的尺寸估计。我们提出了Shapo，这是一种联合多对象检测的方法，3D纹理重建，6D对象姿势和尺寸估计。 Shapo的关键是一条单杆管道，可回归形状，外观和构成潜在的代码以及每个对象实例的口罩，然后以稀疏到密集的方式进一步完善。首先学到了一种新颖的剖面形状和前景数据库，以将对象嵌入各自的形状和外观空间中。我们还提出了一个基于OCTREE的新颖的可区分优化步骤，使我们能够以分析的方式进一步改善对象形状，姿势和外观。我们新颖的联合隐式纹理对象表示使我们能够准确地识别和重建新颖的看不见的对象，而无需访问其3D网格。通过广泛的实验，我们表明我们的方法在模拟的室内场景上进行了训练，可以准确地回归现实世界中新颖物体的形状，外观和姿势，并以最小的微调。我们的方法显着超过了NOCS数据集上的所有基准，对于6D姿势估计，MAP的绝对改进为8％。项目页面：https：//zubair-irshad.github.io/projects/shapo.html