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10/07/2022
Certified machine learning: Rigorous a posteriori error bounds for PDE defined PINNs
Prediction error quantification in machine learning has been left out of...
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04/28/2022
Port-Hamiltonian Dynamic Mode Decomposition
We present a novel physics-informed system identification method to cons...
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03/31/2022
Certified machine learning: A posteriori error estimation for physics-informed neural networks
Physics-informed neural networks (PINNs) are one popular approach to int...
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01/17/2022
Control of port-Hamiltonian differential-algebraic systems and applications
The modeling framework of port-Hamiltonian descriptor systems and their ...
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09/22/2021
Physics-informed Neural Networks-based Model Predictive Control for Multi-link Manipulators
We discuss nonlinear model predictive control (NMPC) for multi-body dyna...
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09/14/2021
Identification of linear time-invariant systems with Dynamic Mode Decomposition
Dynamic mode decomposition (DMD) is a popular data-driven framework to e...
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07/07/2021
Decomposition of flow data via gradient-based transport optimization
We study an optimization problem related to the approximation of given d...
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06/21/2021
Efficient Wildland Fire Simulation via Nonlinear Model Order Reduction
We propose a new hyper-reduction method for a recently introduced nonlin...
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03/30/2021
Parametric model reduction via rational interpolation along parameters
We present a novel projection-based model reduction framework for parame...
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03/24/2021
Passivity preserving model reduction via spectral factorization
We present a novel model-order reduction (MOR) method for linear time-in...
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12/23/2019
Nonlinear Galerkin Model Reduction for Systems with Multiple Transport Velocities
We propose a new model reduction framework for problems that exhibit tra...
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09/08/2019
Semi-explicit discretization schemes for weakly-coupled elliptic-parabolic problems
We prove first-order convergence of the semi-explicit Euler scheme combi...
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08/05/2019