Basic concepts of optimization and structure of mathematical models. Gradient methods. Direct search methods. Gauss-Newton method for parameter optimization in models described by ordinary and partial differential equations. Constrained optimization. Statistical inferences. Design of experiments. This course is not eligible for Credit/D/Fail grading.
Basic concepts of optimization and structure of mathematical models. Gradient methods. Direct search methods. Gauss-Newton method for parameter optimization in models described by ordinary and partial differential equations. Constrained optimization. Statistical inferences. Design of experiments. This course is not eligible for Credit/D/Fail grading.
