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This paper aims to improve Reynolds-averaged Navier Stokes (RANS) turbulence models using a data-driven approach based on machine learning (ML). A special focus is put on determining the optimal input features used for the ML model.

The field inversion and machine learning (FIML) approach is applied to the negative Spalart-Allmaras turbulence model for transonic flows over an airfoil where shock-induced separation occurs.

Optimal input features and an ML model are developed, which improve the existing negative Spalart-Allmaras turbulence model with respect to shock-induced flow separation.

A comprehensive workflow is demonstrated that yields insights on which input features and which ML model should be used in the context of the FIML approach

This study examines the variety of cooperative strategies used to organize the international co-production of motion pictures. Motion picture production is a high-goal singularity, project-based industry in which the structure of relationships between companies involved in cooperative strategies is highly visible. Working from existing theories of co-production and drawing on the strategic joint ventures literature, I examine archival data, first for evidence of the strategies predicted by theory, and then for project participation strategies that theory does not account for. I identify four strategies on the basis of the ways that firms participate in international co-productions. A large number of relatively short-lived firms enact strategies of supplying resources and skills to the persistent firms dominate the industry. Two types of persistent firms cooperate with both direct competitors and complementors but pursue different markets, whereas a third type avoids cooperation with peers. The observed strategies constitute a hierarchy of strategic roles, and thus demonstrate the complexity of strategic behavior involved in project-based production.