DMDgen

DMDgen is the name attributed to several software tools that share a common aim: to generate data, models, or configurations for workflows that involve dynamic mode decomposition (DMD) and related data‑driven modeling approaches. Because “DMDgen” is used by multiple projects, there is no single canonical implementation or specification; each version tends to define its own scope, input formats, and output conventions.

Most implementations describe DMDgen as a generator toolkit that provides templates or modules for creating synthetic time-series data, configuring experiment parameters, and producing ready-to-run configurations for DMD analyses. Typical features include scriptable data generation, support for common data formats (such as CSV or HDF5), lightweight command-line interfaces, and optional integration with scientific computing languages like Python or MATLAB. The intent is to streamline prototyping, testing, and reproducibility in DMD workflows.

The tooling is used primarily by researchers and practitioners in computational science, control theory, and engineering who work with data-driven modeling. Project activity and maintenance levels vary by version; some releases are maintained with active community involvement, while others exist as archived or legacy tools. Documentation quality also varies, with some installations providing tutorials and API references and others offering minimal guidance.

In sum, DMDgen describes a family of generation tools rather than a single product, and its exact capabilities depend on the specific project considered. Users should consult the documentation accompanying a given DMDgen distribution to understand its features, requirements, and license terms.