6 papers
Data-Induced Interactions of Sparse Sensors Using Statistical Physics
Andrei A. Klishin, J. Nathan Kutz, Krithika Manohar
Large-dimensional empirical data in science and engineering frequently have a low-rank structure and can be represented as a combination of just a few eigenmodes. Because of this s…
Sparse identification of nonlinear dynamics and Koopman operators with Shallow Recurrent Decoder Networks
Mars Liyao Gao, Jan P. Williams, J. Nathan Kutz
Modeling real-world spatio-temporal data is exceptionally difficult due to inherent high dimensionality, measurement noise, partial observations, and often expensive data collectio…
Statistical Mechanics of Dynamical System Identification
Andrei A. Klishin, Joseph Bakarji, J. Nathan Kutz +1
Recovering dynamical equations from observed noisy data is the central challenge of system identification. We develop a statistical mechanics approach to analyze sparse equation di…
Reservoir computing for system identification and predictive control with limited data
Jan P. Williams, J. Nathan Kutz, Krithika Manohar
Model predictive control (MPC) is an industry standard control technique that iteratively solves an open-loop optimization problem to guide a system towards a desired state or traj…
Towards a Reliable Offline Personal AI Assistant for Long Duration Spaceflight
Oliver Bensch, Leonie Bensch, Tommy Nilsson +5
As humanity prepares for new missions to the Moon and Mars, astronauts will need to operate with greater autonomy, given the communication delays that make real-time support from E…
Deep Generative Modeling for Identification of Noisy, Non-Stationary Dynamical Systems
Doris Voina, Steven Brunton, J. Nathan Kutz
A significant challenge in many fields of science and engineering is making sense of time-dependent measurement data by recovering governing equations in the form of differential e…