4.4.3. pde.solvers.explicit module

Defines an explicit solver supporting various methods

class ExplicitSolver(pde: PDEBase, scheme: str = 'euler', backend: str = 'auto', adaptive: bool = False, tolerance: float = 0.0001)[source]

Bases: pde.solvers.base.SolverBase

class for solving partial differential equations explicitly

Parameters

  • pde (PDEBase) – The instance describing the pde that needs to be solved

  • scheme (str) – Defines the explicit scheme to use. Supported values are ‘euler’ and ‘runge-kutta’ (or ‘rk’ for short).

  • backend (str) – Determines how the function is created. Accepted values are ‘numpy` and ‘numba’. Alternatively, ‘auto’ lets the code decide for the most optimal backend.

  • adaptive (bool) – When enabled, the time step is adjusted during the simulation using the error tolerance set with tolerance.

  • tolerance (float) – The error tolerance used in adaptive time stepping. This is used in adaptive time stepping to choose a time step which is small enough so the truncation error of a single step is below tolerance.

dt_max = 10000000000.0
dt_min = 1e-10
info: Dict[str, Any]
make_stepper(state: FieldBase, dt=None) → Callable[[FieldBase, float, float], float][source]

return a stepper function using an explicit scheme

Parameters

  • state (FieldBase) – An example for the state from which the grid and other information can be extracted

  • dt (float) – Time step of the explicit stepping. If None, this solver specifies 1e-3 as a default value.

Returns

Function that can be called to advance the state from time t_start to time t_end. The function call signature is (state: numpy.ndarray, t_start: float, t_end: float)

name = 'explicit'