systems.base.core.DiscretizedSystem

systems.base.core.DiscretizedSystem(
    continuous_system,
    dt=0.01,
    method='rk4',
    mode=None,
    interpolation_kind='linear',
    auto_detect_sde=True,
    sde_method=None,
    **integrator_kwargs,
)

Pure wrapper providing discrete interface to continuous systems.

Protocol Satisfaction

This class satisfies: - DiscreteSystemProtocol: Has step(), simulate(), dt, nx, nu - LinearizableDiscreteProtocol: Has linearize() (wraps continuous)

Does NOT satisfy: - SymbolicDiscreteProtocol: No symbolic machinery (purely numerical)

This means it can be used in: - ✓ Any function expecting DiscreteSystemProtocol - ✓ Control design (LQR, MPC) expecting LinearizableDiscreteProtocol - ✗ Code generation expecting SymbolicDiscreteProtocol

Examples

>>> from controldesymulation.types.protocols import LinearizableDiscreteProtocol
>>>
>>> def lqr_design(system: LinearizableDiscreteProtocol, Q, R):
...     Ad, Bd = system.linearize(np.zeros(system.nx), np.zeros(system.nu))
...     # ... LQR computation
>>>
>>> # DiscretizedSystem works here:
>>> continuous = Pendulum(m=1.0, l=0.5)
>>> discrete = DiscretizedSystem(continuous, dt=0.01)
>>> K = lqr_design(discrete, Q, R)  # ✓ Type checks pass!

Methods

Name	Description
change_method	Create new DiscretizedSystem with different method.
get_available_methods	Get available integration methods for a backend.
get_info	Get comprehensive discretization information and configuration.
print_info	Print formatted discretization information.
simulate_stochastic	Simulate stochastic system with multiple Monte Carlo trajectories.

change_method

systems.base.core.DiscretizedSystem.change_method(new_method, **new_kwargs)

Create new DiscretizedSystem with different method.

get_available_methods

systems.base.core.DiscretizedSystem.get_available_methods(
    backend='numpy',
    method_type='all',
)

Get available integration methods for a backend.

Delegates to method_registry.get_available_methods() for the canonical source of truth about method availability.

Parameters

Name	Type	Description	Default
backend	Backend	Backend to query: ‘numpy’, ‘torch’, or ‘jax’	'numpy'
method_type	str	Filter by method type: - ‘all’: All methods (default) - ‘deterministic’: Only ODE methods - ‘stochastic’: Only SDE methods - ‘fixed_step’: Only fixed-step methods (both ODE and SDE) - ‘adaptive’: Only adaptive methods (both ODE and SDE)	'all'

Returns

Name	Type	Description
	dict	Dictionary with method categories and their available methods

Examples

>>> methods = DiscretizedSystem.get_available_methods('torch', 'stochastic')
>>> print(methods['sde_fixed_step'])
['euler', 'milstein', 'srk', 'midpoint']
>>> print(methods['canonical_aliases'])
['euler_maruyama', 'milstein', 'sra1', 'reversible_heun', 'rk45', ...]

See Also

method_registry.get_available_methods : Implementation details

get_info

systems.base.core.DiscretizedSystem.get_info()

Get comprehensive discretization information and configuration.

print_info

systems.base.core.DiscretizedSystem.print_info()

Print formatted discretization information.

simulate_stochastic

systems.base.core.DiscretizedSystem.simulate_stochastic(
    x0,
    u_sequence=None,
    n_steps=100,
    n_trajectories=100,
    **kwargs,
)

Simulate stochastic system with multiple Monte Carlo trajectories.