307 citations · 572 across the 13 of their papers we have counts for
13 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
Many engineering problems involve phenomena whose governing equations are poorly characterized or only partially known. Surrogate modeling techniques such as neural networks can ca…
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…
Data-Driven Modeling for Transonic Aeroelastic Analysis
Nicola Fonzi, Steven L. Brunton, Urban Fasel
Aeroelasticity in the transonic regime is challenging because of the strongly nonlinear phenomena involved in the formation of shock waves and flow separation. In this work, we int…
Benchmarking sparse system identification with low-dimensional chaos
Alan A. Kaptanoglu, Lanyue Zhang, Zachary G. Nicolaou +2
Sparse system identification is the data-driven process of obtaining parsimonious differential equations that describe the evolution of a dynamical system, balancing model complexi…
Convergence of uncertainty estimates in Ensemble and Bayesian sparse model discovery
L. Mars Gao, Urban Fasel, Steven L. Brunton +1
Sparse model identification enables nonlinear dynamical system discovery from data. However, the control of false discoveries for sparse model identification is challenging, especi…
Robust, High-Rate Trajectory Tracking on Insect-Scale Soft-Actuated Aerial Robots with Deep-Learned Tube MPC
Andrea Tagliabue, Yi-Hsuan Hsiao, Urban Fasel +4
Accurate and agile trajectory tracking in sub-gram Micro Aerial Vehicles (MAVs) is challenging, as the small scale of the robot induces large model uncertainties, demanding robust…
The Experimental Multi-Arm Pendulum on a Cart: A Benchmark System for Chaos, Learning, and Control
Kadierdan Kaheman, Urban Fasel, Jason J. Bramburger +3
The single, double, and triple pendulum has served as an illustrative experimental benchmark system for scientists to study dynamical behavior for more than four centuries. The pen…
Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control
Urban Fasel, J. Nathan Kutz, Bingni W. Brunton +1
Sparse model identification enables the discovery of nonlinear dynamical systems purely from data; however, this approach is sensitive to noise, especially in the low-data limit. I…
Data-driven unsteady aeroelastic modeling for control
Michelle Hickner, Urban Fasel, Aditya G. Nair +2
Aeroelastic structures, from insect wings to wind turbine blades, experience transient unsteady aerodynamic loads that are coupled to their motion. Effective real-time control of f…
PySINDy: A comprehensive Python package for robust sparse system identification
Alan A. Kaptanoglu, Brian M. de Silva, Urban Fasel +9
Automated data-driven modeling, the process of directly discovering the governing equations of a system from data, is increasingly being used across the scientific community. PySIN…
Data-driven nonlinear aeroelastic models of morphing wings for control
Nicola Fonzi, Steven L. Brunton, Urban Fasel
Accurate and efficient aeroelastic models are critically important for enabling the optimization and control of highly flexible aerospace structures, which are expected to become p…