arXiv:2512.17534v2 Announce Type: replace-cross Abstract: Modeling and controlling fluids is critical across science and engineering. Effective flow control can increase lift, reduce drag, enhance mixing, and attenuate noise, potentially unlocking new technologies. Yet controlling fluids is hard: the dynamics are high-dimensional, nonlinear, and multiscale. While reinforcement learning (RL) has recently succeeded in robotics and protein folding through shared benchmarks, fluid dynamics has resisted such progress: each controller is typically tuned to a single geometry and operating point, maki
The development of platforms like HydroGym is critical now as AI, specifically reinforcement learning, matures and is increasingly applied to complex scientific and engineering domains like fluid dynamics, which have historically resisted such approaches.
Effective fluid dynamics control has vast implications across various industries, from aerospace to energy, and this platform could accelerate the deployment of AI-driven solutions to critical challenges, leading to significant economic and scientific advancements.
The creation of standardized benchmarks and platforms for AI in fluid dynamics shifts the research landscape from bespoke, single-application solutions to more generalizable and scalable AI controllers.
- · Aerospace industry
- · Energy sector
- · Robotics
- · AI research & development
- · Traditional CFD modelers
Improved efficiency and performance in fluid-dependent systems will become more accessible through AI.
Reduced operational costs and new design paradigms will emerge in areas like aviation, automotive, and industrial processes due to optimized fluid control.
The mastery of complex physical systems via AI could unlock entirely new categories of technology, fundamentally altering the energy and transportation landscapes.
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Read at arXiv cs.LG