Abstract: Abstract ADMS-Urban is a non-linear, static, urban air quality model, with high-dimensional outputs. A simulation of NO 2 and PM 10 concentrations every hour during a full year and over an entire city can take dozens of days of computations, which greatly limits the range of methods that can be applied to the model, especially for uncertainty quantification. This work presents a method to replace the complete model, ADMS-Urban, with a meta-model or surrogate model, i.e., a reasonably close approximation of ADMS-Urban whose computational cost is...
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Topics: 
Applied mathematics
Mathematical optimization
Algorithm