6 papers
An Introduction to Sparse Identification of Nonlinear Dynamics for Engineering Applications
Yao Cheng Li, Ana Larrañaga, Steven L. Brunton +1
Many engineering problems involve phenomena whose governing equations are poorly characterized or only partially known. Surrogate modeling techniques such as neural networks can ca…
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…
How Low Can You Go? Active Learning for Sparse Model Discovery in the Ultra-Low-Data Limit
Ana Larrañaga, Urban Fasel, Steven L. Brunton
Identifying the governing equations of complex dynamical systems remains a fundamental challenge across science and engineering. While early approaches relied on empirical data and…
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…