ENGRCEE 290 Merging Models and Data (2017-2018)

ENGRCEE 290 Merging Models and Data

(Not required for any major.)
Catalog 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)

Required Textbook:
. Edition, , 1969, ISBN-13 978-3642037108.

. Edition, , 1969, ISBN-13 978-0470743614.

Recommended Textbook:
None
References:
None
Coordinator:
Jasper Alexander Vrugt
Relationship to Student Outcomes
No student outcomes specified.
Course Learning Outcomes. Students will:
Prerequisites by Topic

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

Lecture Topics:

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.

Class Schedule:

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

Computer Usage:

Some lectures with computers.

Laboratory Projects:
Professional Component
Design Content Description
Approach:
Lectures:
Laboratory Portion:
Grading Criteria:
  • Midterm: 20%
  • Final: 30%
  • Homework: 20%
  • Modeling Project: 30%
  • Total: 100%
Estimated ABET Category Content:

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

Prepared:
February 22, 2017
Senate Approved:
May 7, 2013
Approved Effective:
2013 Fall Qtr