Explainable deep reinforcement learning reveals energy-efficient control strategies for turbulent drag reduction
arXiv:2606.00949v1 Announce Type: new Abstract: We propose a method combining Multi-Agent Deep Reinforcement Learning (MARL) and eXplainable Deep Learning (XDL) to reduce drag in wall-bounded turbulent flows. Taking as a baseline the results of training agents directly targeting wall-shear stress and opposition control, three SHAP-guided approaches are compared. In the first, the reward is computed from SHAP attributions of a U-net predicting the future velocity field; in the second, from SHAP attributions of a U-net predicting the skin-friction coefficient; in the third, from a combination of
The increasing sophistication of AI models and the pressing need for energy efficiency across industries drive the development of advanced control strategies for complex physical systems.
Advanced AI techniques, like explainable deep reinforcement learning, can unlock significant energy savings in critical industrial processes and potentially accelerate advancements in fluid dynamics and material science.
The ability to apply explainable AI to fluid dynamics allows for more optimized and trustworthy control mechanisms, potentially leading to unprecedented efficiencies in areas like aviation, shipping, and pipeline transport.
- · Logistics & Shipping
- · Aviation Industry
- · Energy Sector
- · AI/ML Research & Development
- · Inefficient industrial processes
- · Traditional fluid dynamics control methods
Reduced energy consumption in turbulent flow applications through AI-driven control.
Accelerated design of more aerodynamically and hydrodynamically efficient vehicles and infrastructure.
New material science innovations inspired by AI-optimized fluid interactions, leading to novel surfaces and coatings.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG