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Found 8 papers, 1 papers with code
Nonlinear embeddings for conserving Hamiltonians and other quantities with Neural Galerkin schemes
This work focuses on the conservation of quantities such as Hamiltonians, mass, and momentum when solution fields of partial differential equations are approximated with nonlinear parametrizations such as deep networks.
Fixed-Order H-Infinity Controller Design for Port-Hamiltonian Systems
In constrast to that, our method computes pH controllers, that are automatically passive and simultaneously aim to minimize the H-infinity norm of the closed-loop transfer function.
Structured Optimization-Based Model Order Reduction for Parametric Systems
We develop an optimization-based algorithm for parametric model order reduction (PMOR) of linear time-invariant dynamical systems.
Structure-Preserving Model Order Reduction for Index Two Port-Hamiltonian Descriptor Systems
Our parameterization ensures that the reduced model is again a pH-DAE system and enables a compact representation of the algebraic part of the large-scale model, which in projection-based methods often requires a more involved treatment.
Adaptive Sampling for Structure Preserving Model Order Reduction of Port-Hamiltonian Systems
We present an adaptive sampling strategy for the optimization-based structure preserving model order reduction (MOR) algorithm developed in [Schwerdtner, P. and Voigt, M. (2020).
Port-Hamiltonian System Identification from Noisy Frequency Response Data
The numerical evaluation demonstrates a substantial increase in accuracy of our method compared to the other pH identification procedure and a slightly improved accuracy compared to vector-fitting.
SOBMOR: Structured Optimization-Based Model Order Reduction
The structural constraints can be encoded in the parametrization of the ROM.
Risk Assessment for Machine Learning Models
In this paper we propose a framework for assessing the risk associated with deploying a machine learning model in a specified environment.
