Spectral Deferred Correction Methods for Second-Order Problems

Python code for implementing the paper’s plots on Second-order SDC methods.

You are welcome to use and adapt this code under the terms of the BSD license. If you utilize it, either in whole or in part, for a publication, please provide proper citation:

@misc{akramov2023spectral,
    title={Spectral deferred correction methods for second-order problems},
   author={Ikrom Akramov and Sebastian Götschel and Michael Minion and Daniel Ruprecht and Robert Speck},
   year={2023},
   eprint={2310.08352},
   archivePrefix={arXiv},
   primaryClass={math.NA}}

Reproducing Figures from the Publication

• Fig. 1: Execute harmonic_oscillator_run_stability.py while setting kappa_max=18 and mu_max=18.

• Fig. 2: Run harmonic_oscillator_run_stability.py with the following configurations:

  • Set kappa_max=30 and mu_max=30.

  • Adjust maxiter to 1, 2, 3, or 4 and execute each individually.

• Table 1: Execute harmonic_oscillator_run_stab_interval.py:

  • For the Picard iteration set: Picard=True

  • To save the results set: save_interval_file=True

• Use the script harmonic_oscillator_run_points.py to create a table based on given $(kappa, mu)$ points. This table assists in determining suitable values for M, K, and quadrature nodes to ensure stability in the SDC method.

  • To save the results set: save_points_file=True

• Fig. 3: Run penningtrap_run_error.py (Run local convergence: conv.run_local_error()) with dt=0.015625/4 and axes=(0,).

• Fig. 4: Run penningtrap_run_error.py (Run local convergence: conv.run_local_error()) using dt=0.015625*4 and axes=(2,).

• Fig. 5: Run penningtrap_run_error.py (Run global convergence: conv.run_global_error()) with dt=0.015625*2:

  • Note: Perform each run individually: first with axes=(0,), then with axes=(2,).

  • Manually set y-axis limits in penningtrap_run_error.py, specifically in lines 147-148.

• Table 2: Execute penningtrap_run_error.py (Run global convergence: conv.run_global_error()) with the following settings:

  • Expected order and approximate order are saved in the file: data/global_order_vs_approx_order.csv

  • Set: K_iter=(2, 3, 4, 10)

  • For M=2:

    • Set dt=0.015625 / 2 and num_nodes=2 (Both Horizontal and Vertical axes)

  • For M=3:

    • Set dt=0.015625 and num_nodes=3 (Both Horizontal and Vertical axes)

  • For M=4:

    • Set dt=0.015625 * 4 and num_nodes=4 (Both Horizontal and Vertical axes)

• Fig. 6: Run penningtrap_run_Hamiltonian_error.py:

  • Note: Execute each computation individually in case of crashes.

  • Data is saved in the data folder.

• Fig. 7: Execute penningtrap_run_work_precision.py with the following configurations:

  • Set dt=0.015625*2 and K_iter=(1, 2, 3) for axis=(2,).

  • Set dt=0.015625*4, and K_iter=(2, 4, 6), and dt_cont=2 for axis=(0,).

Contact

This code is written by Ikrom Akramov