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King Abdullah University of Science and Technology

The King Abdullah University of Science and Technology offers a range of programs and courses.

Programs of Study

The university provides various programs, including:

• Applied Mathematical and Computational Science (AMCS)
• Applied Physics (AP)
• Bioscience (B)
• Biological and Environmental Science and Engineering (BESE)
• Bioengineering (BioE)
• Chemical Engineering (CE)
• Computer, Electrical and Mathematical Sciences and Engineering
• Chemistry (Chem)
• Computer Science (CS)
• Electrical and Computer Engineering (ECE)
• English
• Environmental Science and Engineering (EnSE)
• Energy Resources and Petroleum Engineering (ERPE)
• Earth Science and Engineering (ErSE)
• Marine Science (MarS)
• Mechanical Engineering (ME)
• Material Science and Engineering (MSE)
• Physical Science and Engineering (PSE)
• Plant Science (PS)
• Statistics (STAT)
• Technology Innovation and Entrepreneurship (TIE)
• Winter Enrichment Program (WE)

Courses

The university offers a variety of courses, including those in the Earth Science and Engineering (ErSE) department.

ErSE Courses

The ErSE department offers courses at different levels, including:

• 100 level
• 200 level
  • ErSE 201
  • ErSE 202
  • ErSE 210
  • ErSE 211
  • ErSE 212
  • ErSE 213
  • ErSE 214
  • ErSE 217
  • ErSE 218
  • ErSE 219
  • ErSE 221
  • ErSE 222
  • ErSE 223
  • ErSE 225
  • ErSE 226
  • ErSE 253
  • ErSE 260
  • ErSE 293
  • ErSE 294
  • ErSE 295
  • ErSE 297
  • ErSE 299
• 300 level

ErSE 213 Inverse Problems

Course Description

The ErSE 213 course introduces the principles of inverse theory and data assimilation with applications to geophysics and other sciences. Both deterministic and stochastic viewpoints are covered.

Course Topics

Subjects studied include:

• Least squares
• Generalized inverses
• Regularization
• Kalman filter
• Adjoint method

Course Techniques

Techniques for solving nonlinear inverse and data assimilation problems are also covered.

Credits

The course is worth 3 credits.

Prerequisites

The prerequisites for the course are:

• Linear algebra
• Multivariable calculus
• Probability theory
• MATLAB programming

Level

The course is at the 200 level for Master's students and the 300 level for Ph.D. students, with more home and project work.