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3 units. This course combines themes from mechanics, electromagnetics, thermal physics, and neural networks with an introduction to numerical methods as well as the use of MATLAB. Students will become familiar with the underlying theory for a variety of systems in physics and biology (e.g., harmonic, anharmonic and coupled oscillators; electric fields of electric lenses; geo-thermal power station; and artificial neural networks), derive the necessary mathematical equations describing these systems, learn the necessary numerical methods to solve the underlying equations, and implement the system equations and numerical methods in MATLAB to simulate these systems. As a result, students will be prepared to formulate problems or model systems in physics, biology, and related disciplines, and to solve them numerically or in simulation.
Grading: Regular grades are awarded for this course: A B C D E
Offered: Fall Semester
Numeric Modeling of Physics & Biological Systems, W Fink, Class Notes
Numerical Recipes in C: The Art of Scientific Computing, WH Press, BP Flannery, SA Teukolsky, et al., Cambridge University Press, Cambridge, NY
MATLAB for Engineers, H Moore, 3rd Edition, Pearson
Theoretical Physics on the Personal Computer, EW Schmid, G Spitz, W Loesch, 2nd Edition, Springer, ISBN-10: 3540522433, ISBN-13: 978-3540522430
Neural Networks: An Introduction, B Mueller, J Reinhardt, Berlin: Springer
Introduction to the Theory of Neural Computation (Lecture Notes vol. 1), J Hertz, A Krogh, RG Palmer, Reading, MA: Addison-Wesley
Genetic Algorithms in Search, Optimization and Machine Learning, DE Goldberg, Addison-Wesley, 1989
By the end of this course, the student will be able to:
Class Schedule: Two 75-minute lectures.
Approximately ten homework sets, including MATLAB exercises (411: basic; 511: advanced), during semester. Two midterm and one final written examinations.
(a) an ability to apply knowledge of mathematics, science, and engineering (H)
(b) an ability to design and conduct experiments, as well as to analyze and interpret data (M)
(d) an ability to function on multidisciplinary teams (L)
(e) an ability to identify, formulate, and solve engineering problems (H)
(f) an understanding of professional and ethical responsibility (M)
(i) a recognition of the need for, and an ability to engage in life-long learning (L)
(k) an ability to use the techniques, skills, and modern engineering tools necessary for engineering practice (H).