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Flow control in fluid dynamics represents one of the most complex challenges for modern engineering. The nonlinear, high-dimensional, and multiscale nature of these systems has hindered for decades the creation of controllers capable of adapting to different geometries and operating conditions. Reinforcement learning (RL) has demonstrated revolutionary advances in robotics or protein folding, but its application to fluids faced the lack of standardized environments. HydroGym emerges as a solver-independent platform that provides more than 60 validated environments, from laminar to turbulent flows, with systematic Reynolds number progressions reaching 400,000 and Mach variations in 2D and 3D. This infrastructure allows RL agents to discover robust control principles, such as boundary layer manipulation or wake reorganization, achieving drag reductions greater than 90% in canonical configurations. Most notably is the zero-shot transfer capability: an agent trained on a simplified channel flow achieves a 38% reduction in friction drag on a previously unseen three-dimensional airfoil, with an exploration cost four orders of magnitude lower than direct optimization. This finding suggests that agents discover essential physics rather than configuration-specific patterns, pointing toward generalizable control.
The relevance of HydroGym transcends the academic realm: companies working with fluid dynamics, from aeronautics to energy, can benefit from platforms that integrate artificial intelligence to accelerate the design of new products. At Q2BSTUDIO we develop AI for businesses that allows applying reinforcement learning to complex engineering problems, combining AI agents with high-performance simulations. Additionally, we offer custom applications to implement simulation and control environments, as well as AWS and Azure cloud services to scale computational calculations. Our experience in business intelligence with Power BI allows analyzing the data generated by agents, while cybersecurity ensures the protection of intellectual property in R&D projects. AI agents trained in these environments not only optimize performance but also discover transferable physical principles, opening the door to a new generation of autonomous control solutions.
