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Found 5 papers, 2 papers with code
Robust Recurrent Neural Network to Identify Ship Motion in Open Water with Performance Guarantees -- Technical Report
In this work, we represent a recurrent neural network as a linear time-invariant system with nonlinear disturbances.
Certified machine learning: Rigorous a posteriori error bounds for PDE defined PINNs
Prediction error quantification in machine learning has been left out of most methodological investigations of neural networks, for both purely data-driven and physics-informed approaches.
Certified machine learning: A posteriori error estimation for physics-informed neural networks
Physics-informed neural networks (PINNs) are one popular approach to incorporate a priori knowledge about physical systems into the learning framework.
Physics-informed Neural Networks-based Model Predictive Control for Multi-link Manipulators
We thus present the idea of enhancing PINNs by adding control actions and initial conditions as additional network inputs.
Port-Hamiltonian formulations of poroelastic network models
We investigate an energy-based formulation of the two-field poroelasticity model and the related multiple-network model as they appear in geosciences or medical applications.
Dynamical Systems Optimization and Control 93A30, 65L80, 76S05, 93B52
