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Merten Stender

Ingenieur

Name	Merten Stender
Place	Eutin (Geburtsort) Deutschland
Gender	männlich
Academic degree	M. Sc. Dr.-Ing.
Profession	Ingenieur (characteristic profession)
Related	Technische Universität Hamburg (2016-) Technische Universität Hamburg-Harburg. Dynamics Group (2016-2018) Technische Universität Hamburg. Dynamics Group (2018-)
Source	Homepage: https://cgi.tu-harburg.de/~dynwww/cgi-bin/people/merten-stender/ (Stand: 2018)
Record type	Person
GND-Identifier	1176156292
Record ID	1176156292
Involved in show all Building functional networks for complex response analysis in systems of coupled nonlinear oscillators Data assimilation and parameter identification for water waves using the nonlinear Schrödinger equation and physics-informed neural networks Explainable machine learning determines effects on the sound absorption coefficient measured in the impedance tube In Situ Operando Indicator of Dry Friction Squeal Machine learning-based state maps for complex dynamical systems: applications to friction-excited brake system vibrations Towards neural network‐based numerical friction models Data-driven reduced order modeling for mechanical oscillators using Koopman approaches Data-driven reduced order modeling for mechanical oscillators using Koopman approaches Machine learning for phase-resolved reconstruction of nonlinear ocean wave surface elevations from sparse remote sensing data Towards neural network‐based numerical friction models Up-Net : a generic deep learning-based time stepper for parameterized spatio-temporal dynamics Up-Net: a generic deep learning-based time stepper for parameterized spatio-temporal dynamics Conference Proceedings - Workshop: Adaptive Structures At Shore 2022 : 23rd & 24th of May 2022, Hamburg, Germany Data-driven stability maps for friction induced vibrations Towards neural network-based numerical friction models bSTAB: an open-source software for computing the basin stability of multi-stable dynamical systems Limit cycle computation of self‐excited dynamic systems using nonlinear modes bSTAB: an open-source software for computing the basin stability of multi-stable dynamical systems Exploring the application of reinforcement learning to wind farm control Limit cycle computation of self‐excited dynamic systems using nonlinear modes Spatially localized vibrations in a rotor subjected to flutter Data-driven techniques for the nonlinear dynamics of mechanical structures The basin stability of bi-stable friction-excited oscillators Complex machine dynamics: systematic recurrence quantification analysis of disk brake vibration data Revealing transitions in friction-excited vibrations by nonlinear time-series analysis Reconstruction of governing equations from vibration measurements for geometrically nonlinear systems Recovery of differential equations from impulse response time series data for model identification and feature extraction Characterization of complex states for friction‐excited systems

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Merten Stender

Building functional networks for complex response analysis in systems of coupled nonlinear oscillators

Data assimilation and parameter identification for water waves using the nonlinear Schrödinger equation and physics-informed neural networks

Explainable machine learning determines effects on the sound absorption coefficient measured in the impedance tube

In Situ Operando Indicator of Dry Friction Squeal

Machine learning-based state maps for complex dynamical systems: applications to friction-excited brake system vibrations

Towards neural network‐based numerical friction models

Data-driven reduced order modeling for mechanical oscillators using Koopman approaches

Data-driven reduced order modeling for mechanical oscillators using Koopman approaches

Machine learning for phase-resolved reconstruction of nonlinear ocean wave surface elevations from sparse remote sensing data

Towards neural network‐based numerical friction models

Up-Net : a generic deep learning-based time stepper for parameterized spatio-temporal dynamics

Up-Net: a generic deep learning-based time stepper for parameterized spatio-temporal dynamics

Conference Proceedings - Workshop: Adaptive Structures At Shore 2022 : 23rd & 24th of May 2022, Hamburg, Germany

Data-driven stability maps for friction induced vibrations

Towards neural network-based numerical friction models

bSTAB: an open-source software for computing the basin stability of multi-stable dynamical systems

Limit cycle computation of self‐excited dynamic systems using nonlinear modes

bSTAB: an open-source software for computing the basin stability of multi-stable dynamical systems

Exploring the application of reinforcement learning to wind farm control

Limit cycle computation of self‐excited dynamic systems using nonlinear modes

Spatially localized vibrations in a rotor subjected to flutter

Data-driven techniques for the nonlinear dynamics of mechanical structures

The basin stability of bi-stable friction-excited oscillators

Complex machine dynamics: systematic recurrence quantification analysis of disk brake vibration data

Revealing transitions in friction-excited vibrations by nonlinear time-series analysis

Reconstruction of governing equations from vibration measurements for geometrically nonlinear systems

Recovery of differential equations from impulse response time series data for model identification and feature extraction

Characterization of complex states for friction‐excited systems