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anypinn.catalog.fitzhugh_nagumo

A_KEY = 'a' module-attribute

EPSILON_KEY = 'epsilon' module-attribute

V_KEY = 'v' module-attribute

W_KEY = 'w' module-attribute

FitzHughNagumoDataModule

Bases: PINNDataModule

DataModule for FitzHugh-Nagumo inverse problem.

Generates synthetic data via odeint. Only the voltage v is observed; the recovery variable w is inferred through ODE residuals alone.

Source code in src/anypinn/catalog/fitzhugh_nagumo.py 
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class FitzHughNagumoDataModule(PINNDataModule):
    """DataModule for FitzHugh-Nagumo inverse problem.

    Generates synthetic data via odeint. Only the voltage v is observed;
    the recovery variable w is inferred through ODE residuals alone.
    """

    def __init__(
        self,
        hp: ODEHyperparameters,
        gen_props: ODEProperties,
        noise_std: float = 0.0,
        validation: ValidationRegistry | None = None,
        callbacks: Sequence[DataCallback] | None = None,
    ):
        super().__init__(hp, validation, callbacks)
        self.gen_props = gen_props
        self.noise_std = noise_std

    @override
    def gen_data(self, config: GenerationConfig) -> tuple[Tensor, Tensor]:
        """Generate synthetic FHN data. Returns only v (partially observed)."""

        def fhn_ode(t: Tensor, y: Tensor) -> Tensor:
            return self.gen_props.ode(t, y, self.gen_props.args)

        t = config.x

        sol = odeint(fhn_ode, self.gen_props.y0, t)  # [T, 2]
        v_true = sol[:, 0]

        v_obs = v_true + self.noise_std * torch.randn_like(v_true)

        # (N, 1, 1) — single observed field
        y_data = v_obs.unsqueeze(1).unsqueeze(-1)

        return t.unsqueeze(-1), y_data

gen_props = gen_props instance-attribute

noise_std = noise_std instance-attribute

__init__(hp: ODEHyperparameters, gen_props: ODEProperties, noise_std: float = 0.0, validation: ValidationRegistry | None = None, callbacks: Sequence[DataCallback] | None = None)

Source code in src/anypinn/catalog/fitzhugh_nagumo.py 
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def __init__(
    self,
    hp: ODEHyperparameters,
    gen_props: ODEProperties,
    noise_std: float = 0.0,
    validation: ValidationRegistry | None = None,
    callbacks: Sequence[DataCallback] | None = None,
):
    super().__init__(hp, validation, callbacks)
    self.gen_props = gen_props
    self.noise_std = noise_std

gen_data(config: GenerationConfig) -> tuple[Tensor, Tensor]

Generate synthetic FHN data. Returns only v (partially observed).

Source code in src/anypinn/catalog/fitzhugh_nagumo.py 
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@override
def gen_data(self, config: GenerationConfig) -> tuple[Tensor, Tensor]:
    """Generate synthetic FHN data. Returns only v (partially observed)."""

    def fhn_ode(t: Tensor, y: Tensor) -> Tensor:
        return self.gen_props.ode(t, y, self.gen_props.args)

    t = config.x

    sol = odeint(fhn_ode, self.gen_props.y0, t)  # [T, 2]
    v_true = sol[:, 0]

    v_obs = v_true + self.noise_std * torch.randn_like(v_true)

    # (N, 1, 1) — single observed field
    y_data = v_obs.unsqueeze(1).unsqueeze(-1)

    return t.unsqueeze(-1), y_data