It is our great pleasure to inform you, that today our guest Dr Blagoje Đorđević from Lawrence Livermore National Laboratory held a lecture entitled “Transfer learning and multi-fidelity modeling of laser-driven particle acceleration” at library reading room “Dr. Dragan Popović”. The abstract of the talk was:
Computer models of intense, laser-driven ion acceleration require expensive particle-in-cell (PIC) simulations that may struggle to capture all the multi-scale,multi dimensional physics involved at reasonable costs. Explored is an approach to ameliorate this deficiency using a multi-fidelity model that can incorporate physical trends and phenomena at different levels. As the base framework for this study, an ensemble of approximately 10,000 1D PIC simulations was generated to buttress separate ensembles of hundreds of higher fidelity 1D and 2D simulations. Using transfer learning with deep neural networks, one can reproduce the results of more complex physics at a much smaller cost. The networks trained in this fashion can in turn act as surrogate models for the simulations themselves, allowing for quick and efficient exploration of the parameter space of interest. Standard figures-of-merit were used as benchmarks such as the hot electron temperature, peak ion energy, conversion efficiency, etc. These surrogate models are also useful for incorporating more complex schemes, such as pulse shaping. We can rapidly identify and explore under what conditions dimensionality becomes an important effect and search for outliers in feature space.