MLP-Mixer Based Surrogate Model for Seismic Ground Motion with Spatial Source and Geometry Parameters
Abstract
Seismic motion simulations enable high-precision predictions but are computationally demanding. This study introduces a deep learning surrogate model using the MLP-Mixer architecture to address this challenge. Traditional models using independent Multi-layer Perceptrons (MLPs) fail to capture spatial correlations, while U-shaped Neural Operators (U-NOs) require high computational costs for high-resolution inputs and outputs. Our proposed model, the Multiple MLP-Mixer (Multi-MLP-Mixer), integrates global and local spatial information through multiple MLP-Mixer blocks and dual patch-wise affine transformations. We demonstrate the effectiveness of our method with simulation data from anticipated megathrust earthquakes in the Nankai Trough, achieving performance comparable to state-of-the-art models with significantly improved computational efficiency.
Cite
Text
Hachiya et al. "MLP-Mixer Based Surrogate Model for Seismic Ground Motion with Spatial Source and Geometry Parameters." Proceedings of the 16th Asian Conference on Machine Learning, 2024.Markdown
[Hachiya et al. "MLP-Mixer Based Surrogate Model for Seismic Ground Motion with Spatial Source and Geometry Parameters." Proceedings of the 16th Asian Conference on Machine Learning, 2024.](https://mlanthology.org/acml/2024/hachiya2024acml-mlpmixer/)BibTeX
@inproceedings{hachiya2024acml-mlpmixer,
title = {{MLP-Mixer Based Surrogate Model for Seismic Ground Motion with Spatial Source and Geometry Parameters}},
author = {Hachiya, Hirotaka and Kuroki, Yuto and Iwaki, Asako and Maeda, Takahiro and Ueda, Naonori and Fujiwara, Hiroyuki},
booktitle = {Proceedings of the 16th Asian Conference on Machine Learning},
year = {2024},
pages = {191-206},
volume = {260},
url = {https://mlanthology.org/acml/2024/hachiya2024acml-mlpmixer/}
}