Standard Experiments

Reference for different experiment options

Value Sweep

Value sweeping runs a simulation for each of the specified values. The field in globals.json is populated with the value for each run.

"Radius values": {
"steps": 100,
"type": "values",
"field": "radius",
"values": [0, 1, 2, 3, 4, 5, 6, 7]

Value sweeping is particularly is useful for multi-parameter sweeps and categorical sampling.

Fixed Sample Sweep (linspace)

Fixed sample sweeping or 'linspace' is one of the most common types of parameter sweeps. Define start, stop, and number of samples to generate an even sampling between two values with a set number of data points.

"Radius fixed sample": {
"steps": 100,
"type": "linspace",
"field": "radius",
"start": 0,
"stop": 10,
"samples": 11

Fixed Step Sweep

Instead of using a set number of samples like linspace, arange samples every "increment" between the specified start and stop fields.

"Radius arange": {
"steps": 100,
"type": "arange",
"field": "radius",
"start": 0,
"stop": 10,
"increment": 0.5

Monte Carlo Sweep

Monte Carlo sweeping allows random sampling from a custom distribution. Each supported distribution can be customized through the associated parameters. Each parameter defaults to 1 if not defined.

"Radius monte": {
"steps": 100,
"type": "monte-carlo",
"field": "radius",
"samples": 10,
// Either combination of distributions and parameters:
"distribution": "normal",
"mean": 1,
"std": 1
// or
"distribution": "log-normal",
"mu": 1,
"sigma": 1
// or
"distribution": "poisson",
"rate": 1
// or
"distribution": "beta",
"alpha": 1,
"beta": 1
// or
"distribution": "gamma",
"shape": 1,
"scale": 1

Group Sweep

You can run groups of experiments together by adding experiment keys to the runs array of a group definition. The below code, for example, would execute each of our experiments outlined above as sub-experiments of a new experiment:

"Group Sweep": {
"steps": 100,
"type": "group",
"runs": ["Radius values", "Radius linspace", "Radius arange", "Radius monte"]

Multiparameter Sweep

In order to discover interaction effects in your model, you'll have to perform sweeps over multiple parameters. The multiparameter experiment generates a full factorial design with all of the experiments defined in runs.

"Full factorial sweep": {
"steps": 100,
"type": "multiparameter",
"runs": [
"Radius values",
"Radius linspace",
"Radius arange",
"Radius monte"


Optimization experiments allow you to identify the best combination of parameters for minimizing or maximizing desired metrics.

Read more about Optimization Experiments in the next section of the docs.