How Eqiora fits together

Eqiora keeps the mathematical problem separate from the choices needed to solve it. The Get started example shows every step with one decay model.

Model — what must be true?

Declare quantities, parameters, initial values and equations. Compilation checks their meaning and produces a Model. A time method, mesh or solver is not an equation. Read Models are not simulations for the distinction, or author a model in Python.

Realization — how will it be solved?

Choose numerical methods that support your model. In the decay example, eqiora.resolve selects a time method and returns a Plan. Spatial problems also need the appropriate geometry, mesh and discretization; the steady-flow investigation shows those choices on one concrete problem.

Run — perform the work

Supply the initial state, observation times and other execution inputs required by the Plan, then call eqiora.run. The execution guide explains submitted runs and structured diagnostics.

Result — what happened?

Select an output using the model-bound field handle. For scalar decay, the returned series contains observation times and values. Compare them with a prediction before interpreting a plot. Run and inspect walks through the same decay example.

To decide whether a model answers your physical question, examine its assumptions and compare its predictions with a case you understand. The Learn textbooks build those habits while deriving each model.

Looking inside the implementation?

The contributor architecture overview describes compiler and execution boundaries. For ordinary modeling, start with the task guides above and the Reference.