Oksana Shadura

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Detector simulation is consuming at least half of the HEP computing cycles, and even so, experiments have to take hard decisions on what to simulate, as their needs greatly surpass the availability of computing resources. New experiments still in the design phase such as FCC, CLIC and ILC as well as upgraded versions of the existing LHC detectors will push(More)
Machine learning for complex multi-objective problems (MOP) can substantially speedup the discovery of solutions belonging to Pareto landscapes and improve Pareto front accuracy. Studying convergence speedup of multi-objective search on well-known benchmarks is an important step in the development of algorithms to optimize complex problems such as High(More)
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