Latent-space time evolution of non-intrusive reduced-order models using Gaussian process emulation
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Publication:2115690
DOI10.1016/j.physd.2020.132797OpenAlexW3045014973MaRDI QIDQ2115690
Nesar Ramachandra, Themistoklis Botsas, Indranil Pan, Romit Maulik, Lachlan R. Mason
Publication date: 21 March 2022
Published in: Physica D (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2007.12167
Artificial neural networks and deep learning (68T07) Water waves, gravity waves; dispersion and scattering, nonlinear interaction (76B15) Approximation methods and numerical treatment of dynamical systems (37M99)
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Sequential Bayesian experimental design for estimation of extreme-event probability in stochastic input-to-response systems ⋮ Conditional variational autoencoder with Gaussian process regression recognition for parametric models ⋮ Unsteady reduced order model with neural networks and flight-physics-based regularization for aerodynamic applications ⋮ \textit{FastSVD-ML-ROM}: a reduced-order modeling framework based on machine learning for real-time applications ⋮ Multi-fidelity physics constrained neural networks for dynamical systems
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