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| In this talk I will present several methodologies that serve to analyze uncertainty in the solution of very high dimensional nonlinear discrete inverse problems, and specially the case where the forward problem has a very high computational cost. These different methodologies involve model reduction, sparse deterministic sampling, stochastic sampling and local optimization, and have protected under 2 different USA patents. |
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