From the 1 of 4 linked papers with an AI index.
4 papers
Co-Design of Aeroelastic Systems with Deep Reinforcement Learning
Yao Cheng Li, Urban Fasel
Control co-design considers the physical system and its controller together, enabling the strong coupling between system design and control to be uncovered and exploited. This is e…
An Introduction to Sparse Identification of Nonlinear Dynamics for Engineering Applications
Yao Cheng Li, Ana Larrañaga, Steven L. Brunton +1
The paper presents a tutorial on the Sparse Identification of Nonlinear Dynamics (SINDy) method, showing how sparse regression can uncover interpretable governing equations from sm…
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…
SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning
Nicholas Zolman, Christian Lagemann, Urban Fasel +2
Deep reinforcement learning (DRL) has shown significant promise for uncovering sophisticated control policies that interact in complex environments, such as stabilizing a tokamak f…