2 citations · 2 across the 5 of their papers we have counts for
5 papers
Explainable deep reinforcement learning reveals energy-efficient control strategies for turbulent drag reduction
Federica Tonti, Ricardo Vinuesa
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 baselin…
AeroJEPA: Learning Semantic Latent Representations for Scalable 3D Aerodynamic Field Modeling
Francisco Giral, Abhijeet Vishwasrao, Andrea Arroyo Ramo +8
High-fidelity CFD is essential for aerodynamic design, but repeated simulations are computationally expensive, motivating surrogate models for rapid evaluation across geometries an…
Agentic Exploration of PDE Spaces using Latent Foundation Models for Parameterized Simulations
Abhijeet Vishwasrao, Francisco Giral, Mahmoud Golestanian +8
Flow physics and more broadly physical phenomena governed by partial differential equations (PDEs), are inherently continuous, high-dimensional and often chaotic in nature. Traditi…
Navigation in a Three-Dimensional Urban Flow using Deep Reinforcement Learning
Federica Tonti, Ricardo Vinuesa
Unmanned Aerial Vehicles (UAVs) are increasingly populating urban areas for delivery and surveillance purposes. In this work, we develop an optimal navigation strategy based on Dee…
Navigation in a simplified Urban Flow through Deep Reinforcement Learning
Federica Tonti, Jean Rabault, Ricardo Vinuesa
The increasing number of unmanned aerial vehicles (UAVs) in urban environments requires a strategy to minimize their environmental impact, both in terms of energy efficiency and no…