Speaker
Description
We will be introducing recent machine learning techniques researched and developed at the Institute of Radiation Physics for advanced comprehension of compact Laser-particle accelerators (electrons and ions). High-fidelity simulations of the involved physical phenomena are carried out by computationally-expensive particle-in-cell simulations which are used for planning of experiments as well as subsequent analysis. We will be discussing methods for surrogate modeling and reduced-order modeling for reducing the computational complexity and storage footprint of the simulations. From an experimental perspective, one important task relates to the recovery of the initial physics conditions using simulations that mimic the experiment. In addition, advanced spectral diagnostics provide promising novel insights into time-dependent processes, e.g. inside the plasma. Analysis of such data frequently touches inverse problems (e.g. phase retrieval) that occur in e.g. laser diagnostics, analysis of plasma expansion via pump-probe experiments (SAXS reconstruction), or novel experimental diagnostics such as coherent-transition-radiation. Modern data-driven methods promise fast solutions and quantify uncertainty even of ambiguous inverse problems, while the reliability of these methods on out-of-distribution data has to be considered.
| Physical Presentation | I would be willing present physically. |
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