SED-ML¶
A machine-readable exchange format for specifying reproducible simulation experiments independently of the referenced computational models.
Core Idea¶
SED-ML describes simulation experiments rather than the model equations themselves, support depends on language level and version, and reproducibility also requires resolvable model and data dependencies. An XML document references models, declares modifications, simulations and tasks, computes data generators, and binds outputs so software can reproduce the intended experiment workflow. The abstraction is therefore identified by a declared carrier, a transformation or constraint over that carrier, and an invariant that tells an analyst whether the named structure is genuinely present.
Scope of Application¶
SED-ML belongs to computational modeling and is useful where the analyst can specify the typed computational modeling carrier, including objects, relations, parameters, conventions, evidence, boundaries, and comparison targets, then evaluate the SED-ML level and version, referenced model resources and formats, model changes, simulation algorithms and parameters, tasks and repeated tasks, ranges and variables, data generators, reports or plots, identifiers and namespaces, validation and dependency provenance are explicit. The scope is broad within that domain but bounded by the need for the SED-ML level and version, referenced model resources and formats, model changes, simulation algorithms and parameters, tasks and repeated tasks, ranges and variables, data generators, reports or plots, identifiers and namespaces, validation and dependency provenance are explicit.
Clarity¶
The abstraction clarifies a crowded vocabulary by making the SED-ML level and version, referenced model resources and formats, model changes, simulation algorithms and parameters, tasks and repeated tasks, ranges and variables, data generators, reports or plots, identifiers and namespaces, validation and dependency provenance are explicit the center of the account. A claim should name the carrier, the governing operation or relation, the applicable assumptions, and the recognition test.
Manages Complexity¶
Without the abstraction, an analyst must reason directly over many local details: the carrier roles, admissibility assumptions, competing conventions, derived invariants, boundary cases, and proof or validation obligations specific to SED-ML. SED-ML compresses them into the roles in the structural signature. That compression permits comparison across instances without erasing the variables that determine validity. It also exposes which details may be varied safely and which are constitutive.
Abstract Reasoning¶
- Identify the carrier. State what the elements, states, objects, or observations are: the typed computational modeling carrier, including objects, relations, parameters, conventions, evidence, boundaries, and comparison targets. Reject examples whose alleged carrier belongs to a different problem. 2. Lock the constitutive rule. Express the SED-ML level and version, referenced model resources and formats, model changes, simulation algorithms and parameters, tasks and repeated tasks, ranges and variables, data generators, reports or plots, identifiers and namespaces, validation and dependency provenance are explicit independently of one notation or implementation.
Knowledge Transfer¶
Knowledge transfers strongly among subfields of computational modeling because they reuse the typed computational modeling carrier, including objects, relations, parameters, conventions, evidence, boundaries, and comparison targets, An XML document references models, declares modifications, simulations and tasks, computes data generators, and binds outputs so software can reproduce the intended experiment workflow., and type the carrier, state every parameter and convention in the definition, test that the SED-ML level and version, referenced model resources and formats, model changes, simulation algorithms and parameters, tasks and repeated tasks, ranges and variables, data generators, reports or plots, identifiers and namespaces, validation and dependency provenance are explicit, compare the nearest accepted identity, and report counterexamples, uncertainty, and limiting cases.
Relationships to Other Abstractions¶
Current abstraction SED-ML Domain-specific
Parents (1) — more general patterns this builds on
-
SED-ML is a kind of Encoding And Decoding Prime
The proposed strict upward parent is
prime:encoding_and_decoding.
Hierarchy path (1) — routes to 1 parentless root
- SED-ML → Encoding And Decoding → Transformation → Function (Mapping)
Neighborhood in Abstraction Space¶
SED-ML sits in a crowded region of the domain-specific corpus (40th percentile for distinctiveness): several abstractions share nearly its structure, so a description that fits it tends to fit its neighbors too.
Family — Software Modeling & Program Architecture (45 abstractions)
Nearest neighbors
- Computational model — 0.91
- Systems modeling — 0.90
- Control-flow diagram — 0.89
- Metamodeling — 0.89
- Model-based design — 0.89
Computed from structural-signature embeddings · 2026-09-08