Design of optimal Runge-Kutta methods - FEniCS Project
Design of optimal Runge-Kutta methods - FEniCS Project Design of optimal Runge-Kutta methods - FEniCS Project
Optimizing for the SD spectrum Blue: eigenvalues; Red: RK stability boundary The convex hull of the generated spectrum is used as a proxy to accelerate the optimization process D. Ketcheson (KAUST) 32 / 36
Optimizing for the SD spectrum Primarily optimized for stable step size Secondary optimization for nonlinear accuracy and low-storage (3 memory locations per unknown) D. Ketcheson (KAUST) 33 / 36
- Page 3 and 4: Outline 1 High order Runge-Kutta me
- Page 5 and 6: Solution of hyperbolic PDEs The fun
- Page 7 and 8: Solution of hyperbolic PDEs The fun
- Page 9 and 10: Solution of hyperbolic PDEs The fun
- Page 11 and 12: Time Integration Using a better tim
- Page 13 and 14: Time Integration Using a better tim
- Page 15 and 16: Time Integration Using a better tim
- Page 17 and 18: Runge-Kutta Methods To solve the in
- Page 19 and 20: Outline 1 High order Runge-Kutta me
- Page 21 and 22: The Stability Function For the line
- Page 23 and 24: Absolute Stability For the linear e
- Page 25 and 26: Stability optimization This leads n
- Page 27 and 28: Stability Optimization: a toy examp
- Page 29 and 30: Stability Optimization: a toy examp
- Page 31 and 32: Stability Optimization: one more ex
- Page 33 and 34: Stability Optimization: one more ex
- Page 35 and 36: Nonlinear accuracy Besides the cond
- Page 37 and 38: Strong stability preservation Desig
- Page 39 and 40: Strong stability preservation Desig
- Page 41 and 42: The Forward Euler condition Recall
- Page 43 and 44: Runge-Kutta methods as a convex com
- Page 45 and 46: Example: A highly oscillatory flow
- Page 47 and 48: Low storage methods 3S Algorithm S3
- Page 49 and 50: Two-step optimization process Our o
- Page 51 and 52: Two-step optimization process Our o
- Page 53: Optimizing for the SD spectrum On r
- Page 57 and 58: Application: flow past a wedge Dens
Optimizing for the SD spectrum<br />
Primarily optimized for stable step size<br />
Secondary optimization for nonlinear accuracy and low-storage (3<br />
memory locations per unknown)<br />
D. Ketcheson (KAUST) 33 / 36