Glossary

A–M

Alignment

A mapping from the features of a dataset to the state variables, parameters, and initial conditions of a model. The alignment supports constructing configurations of a simulation. The SIR compartmental model and a training dataset with features truth-incident_cases, truth-incident_deaths, truth-incident_hospitalization can have the following model-data alignment:

{
    "S": null,
    "I": null,
    "R": null,
    "I_obs": null,
    "N": null,
    "R_frac": null,
    "ℜ₀": null,
    "inc_I_obs": "truth-incident_cases",
    "inc_D": "truth-incident_deaths",
    "inc_H": "truth-incident_hospitalization"
}

Note

Incident refers to new occurrences, while "prevalent" refers to current (new and preexisting) occurrences.

Calibration

A form of statistical inference for determining or updating the value (point estimate or posterior distribution) of model parameters given a reference dataset of observations. The result is typically selected to balance trade-offs between consistency with the modeler's expert knowledge and the "fit" of model observables to the dataset. In advanced cases, other selection criteria can include robustness to model misspecification, interpretability, focus on one statistical quantity of interest, and data privacy and security.

Configuration

Any set of values used as input for a simulator. A configuration is a simulation and model-specific representation of a scenario.

Downscaling

In climate science, the process of combining high-resolution observational data with low-resolution models to generate high-resolution simulation results that would otherwise be too coarse to accurately capture the dynamics of large-scale phenomena such as hurricanes.

Fitting

See Calibration.

Gene regulatory network

See RegNet.

GNR

See RegNet.

Hyperparameter

A quantity that's an input of a simulator. Hyperparameters can't be inferred from data and can impact the precision and accuracy of the resulting simulation. loss, penalty, and tol are hyperparameters of the stochastic gradient descent algorithm .

Inference

See Calibration.

Initial condition

A parameter that corresponds to the value of a state variable at a starting time point. In a model, there are as many initial conditions as state variables. S₀, I₀, and R₀ are initial conditions of the SIR compartmental model.

Lineage graph

A subgraph of the provenance graph. A lineage graph tracks the versioning of an artifact, containing all the data processing steps that lead to its creation.

Model

An abstract representation that approximates the behavior of a system. For example, a set of ordinary differential equations can approximate the course of an epidemic.

Model template

A template is a piece of a model that depicts a common transition for a variable or group of variables. Model templates can be used to quickly edit or create a model. For each model framework, the available model templates make up a list of all the possible states and transitions.

Depending on the modeling framework, available model templates may include:

• Natural conversion
• Natural production
• Natural degradation
• Controlled conversion
• Controlled production
• Controlled degradation
• Grouped controlled conversion
• Grouped controlled production
• Grouped controlled degradation
• Natural replication
• Controlled replication

Modeling

The process of building a model or a simulation.

Modeling framework

Examples include PetriNets, gene regulatory networks (regnets), stock and flow diagrams, and agent-based models (ABMs). Terarium uses the JSON serialization schemas defined in the ASKEM Model Representations repository.

N–Z

Observable

A quantity of a system (and corresponding model) that's measurable as an "observation" data point. The SIR compartmental model has the following observables:

• I_obs (observed infected population)
• N (total population)
• R_frac (recovered population fraction)
• ℜ₀ (basic reproduction ratio )
• inc_I_obs (observed incident infection rate)

Observation function

A function that maps state variables to an observable, capturing knowledge such as the physics of the observation or measurement process and expert heuristics. For example:

I_obs = 0.50 * I
N = S + I + R
R_frac = R / N
ℜ₀ = β * S / γ
inc_I_ob = diff(I_obs(t), t)) * Heaviside(diff(I_obs(t), t))

For convenience, an observation function may refer to observables in many cases.

Operator

An operator in the workflow graph is a node that can represent a model, dataset, or document or a scientific modeling process that modifies or executes project resources.

Operators that perform scientific modeling processes have two configuration methods. A wizard view exposes the most common configuration options, while a notebook view provides a direct interface to writing and editing executable code. Additionally, an integrated AI assistant can generate code based on natural language input.

Optimization

The process of determining the values of variables that minimize or maximize some objectives subject to constraints. These variables typically represent possible interventions to achieve an outcome (for example, adjusting the duration of a masking policy to reduce the number of hospitalized individuals). In risk-based optimization under uncertainty (RBOUU), the objectives and constraints can be functions of distributions of model parameters and outputs. For example, the constraint that the probability of a super-spreader event never exceeds a threshold value.

Note

Fitting can involve optimization ("optimal fitting" and "constrained optimal fitting") but not necessarily (approximation with a "particle filter").

Parameter

A fixed quantity of a model. Parameters consist of the constants internal to the model. They can be inferred from data made from observations of the underlying system. β and γ are parameters of the SIR compartmental model and weights of an artificial neural network model.

PetriNet

A PetriNet---or place/transition network---is a modeling framework that represents the dynamic behavior of a system. Circular nodes represent variables or places taken at states or compartments. Square nodes represent transitions. Edges between nodes show the flow of variables or places through various transitions.

A PetriNet could, for example, represent populations of people transitioning between the different states in an SIR (susceptible/infected/recovered) model.

Provenance graph

A directed graph constructed from all the artifacts created by the workflow (as nodes) and the relations between them (as links):

	Relation Type	Source Node Type	Target Node Type
0	COPIED_FROM	Model	Model
1	COPIED_FROM	ModelRevision	ModelRevision
2	GLUED_FROM	Model	Model
	...	...	...
32	IS_CONCEPT_OF	Concept	Dataset

RegNet

A RegNet---or gene regulatory network (GNR)---is a modeling framework commonly used in systems biology. Nodes (or vertices) represent genes, proteins, or chemicals, while edges represent regulatory relationships or interactions between them.

Result

The output of a simulation (partial or complete).

Run

An execution of a simulation.

Scenario

A natural-language description of the context, problems, or questions that require a modeling and simulation process.

Given a scenario, you can construct a simulation and a configuration through modeling to execute a run and generate a result. During a run, the simulator has states and values (running time, memory usage) incidental to the simulation that are not considered part of the result, but that may influence future model and simulation selection.

Simulating

The process of executing a simulation on a simulator.

Simulation

An executed instance of a model. A simulation suggests the behavior of the underlying system under specific conditions. What makes a model executable depends on the details of the model, the simulator, and the goals of the people involved.

Simulator

A program that runs a model with specific input values and generates output values.

State variable

A varying quantity of a system and corresponding model. In combination with others, these variables can fully determine the "state" of the underlying system. S, I, and R are state variables of the SIR compartmental model.

Stock and flow

Stock and flow is a modeling framework commonly used in system dynamics. Nodes represent stocks or reservoirs that accumulate over time. Edges between stocks flow into valves that represent how accumulations change.

Strata model

A model that captures the fine-grained interactions between the different strata state variables. Examples include infectious contact between subpopulations of different age groups and travel by individuals between different locations.

Stratification

The process of dividing the populations of a model into subsets (subpopulations or strata), often along demographic characteristics such as age and location. The goal is to include more fine-grained interactions—those between the strata—into the model.

It is herein implemented as a kind of "typed" Cartesian product between the graph representation of a model P and that of one or many "strata models" Q. The stratified model G has:

• A node for every pair of nodes in P and Q of the same type.
• A link for every link in P or Q with the same pair of node types.

Training

See Calibration.

Workflow graph

A data-flow diagram made up of high-level operators that represent resources and scientific modeling processes such as configure model, calibrate model, and simulate model.