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Home » Applied Sciences » Earth, Atmospheric, and Planetary Sciences » Aeronautics and Astronautics (M-I-T) » Computational Methods in Aerospace Engineering (M-I-T) » Session 21: Probabilistic Methods & Optimization: Statistical sampling methods (M-I-T)

Session 21: Probabilistic Methods & Optimization: Statistical sampling methods (M-I-T)

Course:Computational Methods in Aerospace Engineering (M-I-T)
Discipline:Applied Sciences
Institute:MIT
Instructor(s): Prof. Karen Willcox, Prof. Qiqi Wang
Level:Undergraduate

Computational Methods in Aerospace Engineering (M-I-T)

Session 10: Numerical Methods of PDEs: Finite Volume Methods 1 (M-I-T)
Session 11: Numerical Methods of PDEs: Finite Volume Methods 2 (M-I-T)
Session 12: Numerical Methods of PDEs: Finite Volume in 2D and Midterm Review (M-I-T)
Session 15: Numerical Methods of PDEs: Collocation and Weighted Residuals (M-I-T)
Session 16: Numerical Methods of PDEs: Finite Element Method 1 (M-I-T)
Session 1: Course introduction and ordinary differential equations (ODEs) (M-I-T)
Session 21: Probabilistic Methods & Optimization: Statistical sampling methods (M-I-T)
Session 23: Probabilistic Methods & Optimization: Intro to design optimization I (M-I-T)
Session 24: Probabilistic Methods & Optimization: Intro to design optimization II (M-I-T)
Session 25: Probabilistic Methods & Optimization: Final exam review (M-I-T)
Session 2: Numerical Integration of ODEs: Approximating time derivative (M-I-T)
Session 3: Numerical Integration of ODEs: Accuracy and a pendulum problem (M-I-T)
Session 4: Numerical Integration of ODEs: Stability and a ballistics problem (M-I-T)
Session 5: Numerical Integration of ODEs: Nonlinear Systems (M-I-T)
Session 6: Numerical Integration of ODEs: Stiffness, Implicit Methods, Runge-Kutta (M-I-T)
Session 7: Numerical Methods of Partial Differential Equations: Introduction (M-I-T)
Session 8: Numerical Methods of PDEs: Finite Difference Methods 1 (M-I-T)

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