April 29, 2026 Software Open Access

Creutzburg, Sascha; Rustamov, Jeyhun; Bornschein, Jens; Ganeva, Marina; Gerlach, Alexander; Häusler, Stefan; Helm, Bernd; Hinderhofer, Alexander; Juzak, Robert; Koutsioumpas, Alexandros; Munteanu, Valentin; Pandit, Vedhas; Schreiber, Frank; Mothes, Nico; Kelling, Jeffrey

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    "description": "<p>VIPR (Versatile Inverse Problem Software Framework) is a plugin-based framework for reproducible machine-learning-driven solutions to scientific inverse problems. It addresses ill-posed reconstruction tasks caused by loss of phase information during measurement, where direct inversion is not possible. It implements a modular architecture with domain-specific plugins to produce configurable machine learning workflows, including both deterministic and probabilistic models. Workflows are defined via declarative YAML configurations and can be executed through a command-line interface or a containerized web application. For a given experimental dataset, VIPR produces standardized analysis artifacts, including visualizations and statistical summaries.</p>", 
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