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Found 4 papers, 2 papers with code
Data-Driven Model Reduction and Nonlinear Model Predictive Control of an Air Separation Unit by Applied Koopman Theory
Further, we present an NMPC implementation that uses derivative computation tailored to the fixed block structure of reduced Koopman models.
A Recursively Recurrent Neural Network (R2N2) Architecture for Learning Iterative Algorithms
Meta-learning of numerical algorithms for a given task consists of the data-driven identification and adaptation of an algorithmic structure and the associated hyperparameters.
Hearts Gym: Learning Reinforcement Learning as a Team Event
Amidst the COVID-19 pandemic, the authors of this paper organized a Reinforcement Learning (RL) course for a graduate school in the field of data science.
Personalized Algorithm Generation: A Case Study in Learning ODE Integrators
As a case study, we develop a machine learning approach that automatically learns effective solvers for initial value problems in the form of ordinary differential equations (ODEs), based on the Runge-Kutta (RK) integrator architecture.
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