# ENGRCEE 290 Merging Models and Data (2017-2018)

#### ENGRCEE 290 Merging Models and Data

**ENGRCEE 290 Merging Models and Data (Credit Units: 4)** Introduction to optimization and data assimilation methods for model calibration and uncertainty quantification. Local, global and multi-method search. Bayesian analysis using Monte Carlo simulation with Random Walk Metropolis, and adaptive samplers. Filtering methods for recursive inference. Illustration using environmental models. Graduate students only. (Design units: 0)

Introduction to mathematics (linear and nonlinear programming + partial and ordinary differential equations), MATLAB Programming, Problem Solving in Engineering, Introduction to Statistics, particularly Bayes Law.

Local and global optimization, Genetic Algorithms, Evolutionary Computation, Bayesian Statistics, Kalman Filtering, Markov Chain Monte Carlo simulation, Sequential data Assimilation. These different methods are used for merging environmental / hydrologic / hydro-meteorological / ecological / geophysical models with data. A focus of the study will be on hydrological, land surface, and ecological models, involving the rainfall-runoff transformation, vadose zone hydrology (soil physics), land-surface models predicting flux of latent and sensible heat between the soil surface, canopy and atmosphere, and optimality based ecological models of vegetation growth and dynamics.

Meets for 3 hours of lecture and 1 hour of discussion each week for 10 weeks.

Some lectures with computers.

- Midterm: 20%
- Final: 30%
- Homework: 20%
- Modeling Project: 30%
- Total: 100%

Mathematics and Basic Science: 0.0 credit units

Computing: 0.0 credit units

Engineering Topics: 0.0 credit units

Engineering Science: 0.0 credit units

Engineering Design: 0.0 credit units