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ENERGY 284: Optimization and Inverse Modeling

Treatment of deterministic and stochastic optimization, gradient-based optimization, polytopy method, generalized least squares, non-linear least squares and confidence intervals by numerical methods and bootstrap. Adjoint method for gradient calculation. Genetic algorithms and simulated annealing. Development of proxy functions using regression techniques and neural networks. Application of optimization methods to solving non-linear inverse problems. Baysian method, rejection sampling, metropolis sampling, uncertainty quantification. Parameterization of high-dimensional problems through various expansion techniques. Examples of various Earth sciences inverse problems including flow and wave equations.nnnRequirements: CME 106 and 200 (or equivalent courses)
Terms: Aut | Units: 3 | Repeatable for credit | Grading: Letter (ABCD/NP)
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