Prediction and inference of dynamics in quantum plasmas

Quantum plasmas arise in a range of physical contexts, from planetary interiors to Inertial Confinement Fusion experiments. We require sophisticated numerical approaches to find the structural and dynamic properties of these complex, many-body quantum systems; diverse in accuracy and applicability,...

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书目详细资料
主要作者: Larder, B
其他作者: Gregori, G
格式: Thesis
语言:English
出版: 2020
主题:
实物特征
总结:Quantum plasmas arise in a range of physical contexts, from planetary interiors to Inertial Confinement Fusion experiments. We require sophisticated numerical approaches to find the structural and dynamic properties of these complex, many-body quantum systems; diverse in accuracy and applicability, these computational approaches each have their own strengths and (often ignored) weaknesses. By combining existing techniques with newly developed algorithms, this thesis addresses some weaknesses that are common to many state-of-the-art calculation methods for quantum plasmas, and opens up paths to calculating properties that were not previously accessible. The focus of the thesis is a method based on Bohmian mechanics for dynamic structure calculations; alongside this, stochastic gradient Markov Chain Monte Carlo methods are developed for inferring plasma properties from experimental data.