6 papers
A hybrid proper orthogonal decomposition and diffusion framework for reduced-order forecasting of turbulent flow dynamics
Rodrigo Abadia-Heredia, Xiangrui Zou, Manuel Lopez-Martin +2
Forecasting turbulent flow dynamics requires a balance between predictive fidelity and computational efficiency. Diffusion-based generative models can represent complex spatiotempo…
Divergence-aware adaptive prediction framework for accelerating CFD simulations of unsteady flows
Xiangrui Zou, Zhuoqun Zhao, Guillermo Barragán +1
Reliable long-horizon prediction remains a challenge for data-driven CFD surrogates, because offline-trained models accumulate autoregressive errors and lose accuracy when operatin…
AeroJEPA: Learning Semantic Latent Representations for Scalable 3D Aerodynamic Field Modeling
Francisco Giral, Abhijeet Vishwasrao, Andrea Arroyo Ramo +8
Aerodynamic surrogate models are increasingly used to replace repeated high-fidelity CFD evaluations in many-query design settings, but current approaches still face two important…
Coherent structures in Newtonian and viscoelastic turbulent planar jets
Christian Amor, Adrián Corrochano, Giovanni Soligo +2
The addition of a small amount of long-chain polymers confers viscoelastic properties to Newtonian flows. The resulting non-Newtonian solution now exhibits different dynamics, such…
MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems
Guillermo Barragán, Ashton Hetherington, Arindam Sengupta +3
We introduce MoTIF, a mode-structured tensor framework for multi-parametric approximation, super-resolution, and temporal forecasting of high-dimensional unsteady systems. The meth…
Intercepting Unauthorized Aerial Robots in Controlled Airspace Using Reinforcement Learning
Francisco Giral, Ignacio Gómez, Soledad Le Clainche
The proliferation of unmanned aerial vehicles (UAVs) in controlled airspace presents significant risks, including potential collisions, disruptions to air traffic, and security thr…