Work

Predicting dynamics in parameter-time domain with deep learning and data augmentation

Year: 2024

Type: article

Abstract: The poster for Model Reduction and Surrogate Modeling (MORe2024), presaved for a preprint paper. We propose a non-intrusive parametric MOR method named Data-Augmented Predictive Deep Neural Network (DAPredDNN) that employing CAE-FFNN and KDMD for dynamics prediction in both the time and the parameter space. The proposed method enriches original data by augmenting the KDMD-decoder-extrapolated data during the offline stage. Training the CAE-FFNN with this augmented data allows the network to correlates time-parameter pairs with their extrapolated solution directly, avoiding the time-marching behaviour during the online phase.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.13862175

Authors: Sun, Shuwen, Feng, Lihong, Benner, Peter

Institutions: Max Planck Institute for Dynamics of Complex Technical Systems, Otto-von-Guericke-Universität Magdeburg

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W6893021426

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