Glossary

itis-sumo uses one word per concept, everywhere: in the public API, in these docs, in docstrings, in tests. If you see a synonym, it is drift and it is a bug.

Data shape

Term	Means	Not
sample	One row of tabular data — a single observation.	"job", "point", "record"
variable, parameter	One input column.	"feature", "factor"
response, quantity of interest (QoI)	One output column.	"target", "label"

variable and parameter are interchangeable, as are response and quantity of interest. Inputs and outputs stay distinguished throughout, because they receive different downstream treatment — normalization, sign handling, sampling roles, and their position in a Sobol' decomposition all depend on which side a column is on.

itis-sumo takes plain tabular data: a pandas.DataFrame, or an itis-sumo dataclass that converts trivially to one. It has no notion of an oSPARC FunctionJob. Converting job records into a table — and filtering out the ones that did not complete — belongs to the consumer. Deciding that the resulting table has too few samples to build a surrogate belongs to itis-sumo, which raises so the consumer can surface the problem.

Two things that are never the same thing

Term	Means	Can it be inferred from your samples?
domain	Where a variable may be drawn from or explored — its bounds and scale. A property of the design space.	Yes. Observed bounds plus a detected scale are a defensible default.
distribution	The real-world shape of a variable. A claim about the world.	No. A training set sampled uniformly over a box tells you nothing about whether the real input is normal.

Conflating these is the single easiest way to produce a confidently wrong uncertainty band. Sobol' analysis and MOGA optimization want a domain — a region to explore. Uncertainty propagation and correlation analysis want a distribution — something to draw from. They are configured separately.

Modeling

Term	Means
surrogate, SuMo	The trained metamodel that stands in for an expensive simulation.
surrogate model ID (sumo_model_id)	The server-minted UUID identifying a persisted surrogate in the model store.

What you pass, and what stays hidden

You pass samples and configuration. Everything else is itis-sumo's business: training-file layout, Dakota configuration, normalization, sign handling, internal variable renaming, run directories, and mapping predictions back to your original units.

Sensible defaults are generated for you, so the common path requires no configuration at all. Advanced users may override them — but only in terms of the domain, saying things like scale="log" or direction="maximize". The transforms those choices imply are an implementation detail and never appear in a signature. You can always read back the configuration that was actually used; you cannot set it in transform terms.