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28 Einträge gefunden

Up-Net : a generic deep learning-based time stepper for parameterized spatio-temporal dynamics
2023 - TUHH Open Research - frei zugänglich
Data-driven reduced order modeling for mechanical oscillators using Koopman approaches
2023 - TUHH Open Research - frei zugänglich
Towards neural network‐based numerical friction models
2023 - TUHH Open Research - frei zugänglich
Surface similarity parameter : a new machine learning loss metric for oscillatory spatio-temporal data
2022 - TUHH Open Research - frei zugänglich
A Novel Approach for the Frequency Shift of a Single Component Eigenmode through Mass Addition in the Context of Brake Squeal Reduction
2022 - TUHH Open Research
bSTAB: an open-source software for computing the basin stability of multi-stable dynamical systems
1. Stender, Merten
2. Hoffmann, Norbert
2022 - TUHH Open Research - frei zugänglich
Towards neural network-based numerical friction models
2022 - TUHH Open Research - frei zugänglich
Application of machine learning for the generation of tailored wave sequences
2022 - TUHH Open Research
Data-driven stability maps for friction induced vibrations
2022 - TUHH Open Research - frei zugänglich
Explainable machine learning determines effects on the sound absorption coefficient measured in the impedance tube
2021 - TUHH Open Research
Spatially localized vibrations in a rotor subjected to flutter
2021 - TUHH Open Research - frei zugänglich
Deep learning for brake squeal: Brake noise detection, characterization and prediction
2021 - TUHH Open Research
Explainable machine learning: A case study on impedance tube measurements
2021 - TUHH Open Research
The extended periodic motion concept for fast limit cycle detection of self-excited systems
2020 - TUHH Open Research
Hyperchaos co-existing with periodic orbits in a frictional oscillator
2020 - TUHH Open Research
Deep learning for predicting brake squeal
1. Stender, Merten
2. Hoffmann, Norbert
2020 - TUHH Open Research
Code: Explainable machine learning determines effects on the sound absorption coefficient measured in the impedance tube
2020 - TUHH @ Zenodo - frei zugänglich
The basin stability of bi-stable friction-excited oscillators
2020 - TUHH Open Research - frei zugänglich
Reconstruction of governing equations from vibration measurements for geometrically nonlinear systems
2019 - TUHH Open Research - frei zugänglich
Establishing a common database of ice experiments and using machine learning to understand and predict ice behavior
2019 - TUHH Open Research

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

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

Towards neural network‐based numerical friction models

Surface similarity parameter : a new machine learning loss metric for oscillatory spatio-temporal data

A Novel Approach for the Frequency Shift of a Single Component Eigenmode through Mass Addition in the Context of Brake Squeal Reduction

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

Towards neural network-based numerical friction models

Application of machine learning for the generation of tailored wave sequences

Data-driven stability maps for friction induced vibrations

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

Spatially localized vibrations in a rotor subjected to flutter

Deep learning for brake squeal: Brake noise detection, characterization and prediction

Explainable machine learning: A case study on impedance tube measurements

The extended periodic motion concept for fast limit cycle detection of self-excited systems

Hyperchaos co-existing with periodic orbits in a frictional oscillator

Deep learning for predicting brake squeal

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

The basin stability of bi-stable friction-excited oscillators

Reconstruction of governing equations from vibration measurements for geometrically nonlinear systems

Establishing a common database of ice experiments and using machine learning to understand and predict ice behavior