Towards blending physics-based numerical simulations and seismic databases using generative adversarial network

DOI10.1016/J.CMA.2020.113421zbMath1506.86013OpenAlexW3090207777MaRDI QIDQ2021045

Didier Clouteau, Filippo Gatti

Publication date: 26 April 2021

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cma.2020.113421

zbMATH Keywords

artificial neural networksHPCadversarial generative networksphysics-based simulations

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Seismology (including tsunami modeling), earthquakes (86A15) Computational methods for problems pertaining to geophysics (86-08)

Related Items (1)

AI in computational mechanics and engineering sciences

Uses Software

• GAUSSIAN
• darch
• Adam
• CycleGAN
• pix2pix
• WaveNet
• Wasserstein GAN
• InfoGAN

Cites Work

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• Data-driven probability concentration and sampling on manifold
• New least-square algorithms
• Reducing the Dimensionality of Data with Neural Networks
• Polynomial Chaos Expansion of a Multimodal Random Vector
• Inverse Problem Theory and Methods for Model Parameter Estimation
• Advanced Lectures on Machine Learning

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