590 citations · 590 across the 6 of their papers we have counts for
6 papers
Weak Dominant Balance for Robust Identification of Dynamically Consistent Fluid Flow Structure
Samuel Ahnert, Esther Lagemann, H. Jane Bae +4
Extracting interpretable, localized physical mechanisms from complex spatiotemporal data is a foundational challenge across physics, biology, and engineering, but has remained out…
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…
Explainable AI: Learning from the Learners
Ricardo Vinuesa, Steven L. Brunton, Gianmarco Mengaldo
Artificial intelligence now outperforms humans in several scientific and engineering tasks, yet its internal representations often remain opaque. In this Perspective, we argue that…
X-CAL: Explaining latent causality in physical space for fluid mechanics
Marcial Sanchis-Agudo, Andrés Cremades, Alvaro Martinez-Sanchez +2
We present X-CAL, a pipeline that combines a -variational autoencoder (-VAE) with the synergistic-unique-redundant decomposition (SURD)~\cite{surd} approach for causality ana…
Enhancing Computational Fluid Dynamics with Machine Learning
Ricardo Vinuesa, Steven L. Brunton
Machine learning is rapidly becoming a core technology for scientific computing, with numerous opportunities to advance the field of computational fluid dynamics. In this Perspecti…