AdditiveNoise

experimental.network_dynamics.noise.AdditiveNoise(
    apply_to=None,
    key=None,
    **kwargs,
)

Additive Gaussian noise: sigma * dW_t.

Simple additive white noise with constant variance. The diffusion coefficient is constant across time and states.

Parameters: sigma: Standard deviation of the noise (default: 0.1) Can be scalar (same for all states) or array (per-state)

Note: When converting from TVB’s HeunStochastic integrator: TVB uses ‘nsig’ parameter which is NOT the standard deviation. The conversion is: nsig_tvb = 0.5 * sigma^2 where sigma is the standard deviation used here.

Methods

Name	Description
diffusion	Compute constant diffusion coefficient.

diffusion

experimental.network_dynamics.noise.AdditiveNoise.diffusion(t, state, params)

Compute constant diffusion coefficient.

Args: t: Current time (unused for additive noise) state: Current state, shape [n_states, n_nodes] params: Noise parameters with ‘sigma’

Returns: Raw diffusion coefficient(s) - broadcasting handled by network

# AdditiveNoise { #tvboptim.experimental.network_dynamics.noise.AdditiveNoise }

```python
experimental.network_dynamics.noise.AdditiveNoise(
    apply_to=None,
    key=None,
    **kwargs,
)
```

Additive Gaussian noise: sigma * dW_t.

Simple additive white noise with constant variance.
The diffusion coefficient is constant across time and states.

Parameters:
    sigma: Standard deviation of the noise (default: 0.1)
           Can be scalar (same for all states) or array (per-state)

Note:
    When converting from TVB's HeunStochastic integrator:
    TVB uses 'nsig' parameter which is NOT the standard deviation.
    The conversion is: nsig_tvb = 0.5 * sigma^2
    where sigma is the standard deviation used here.

## Methods

| Name | Description |
| --- | --- |
| [diffusion](#tvboptim.experimental.network_dynamics.noise.AdditiveNoise.diffusion) | Compute constant diffusion coefficient. |

### diffusion { #tvboptim.experimental.network_dynamics.noise.AdditiveNoise.diffusion }

```python
experimental.network_dynamics.noise.AdditiveNoise.diffusion(t, state, params)
```

Compute constant diffusion coefficient.

Args:
    t: Current time (unused for additive noise)
    state: Current state, shape [n_states, n_nodes]
    params: Noise parameters with 'sigma'

Returns:
    Raw diffusion coefficient(s) - broadcasting handled by network