POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition

28 January 2021

Papers citing "POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition"

21 / 21 papers shown

Title
Interpretable Spatial-Temporal Fusion Transformers: Multi-Output Prediction for Parametric Dynamical Systems with Time-Varying Inputs Shuwen Sun Lihong Feng P. Benner 40 0 0 01 May 2025
Verification and Validation for Trustworthy Scientific Machine Learning John D. Jakeman Lorena A. Barba J. Martins Thomas O'Leary-Roseberry AI4CE 56 0 0 21 Feb 2025
Recurrent Deep Kernel Learning of Dynamical Systems N. Botteghi Paolo Motta Andrea Manzoni P. Zunino Mengwu Guo 23 1 0 30 May 2024
Deep polytopic autoencoders for low-dimensional linear parameter-varying approximations and nonlinear feedback design Jan Heiland Yongho Kim Steffen W. R. Werner BDL 32 0 0 26 Mar 2024
Polytopic Autoencoders with Smooth Clustering for Reduced-order Modelling of Flows Jan Heiland Yongho Kim AI4CE 24 2 0 19 Jan 2024
Optimal Transport-inspired Deep Learning Framework for Slow-Decaying Kolmogorov n-width Problems: Exploiting Sinkhorn Loss and Wasserstein Kernel M. Khamlich F. Pichi G. Rozza 21 4 0 26 Aug 2023
Branched Latent Neural Maps M. Salvador Alison Lesley Marsden 30 4 0 04 Aug 2023
Deep Learning-based surrogate models for parametrized PDEs: handling geometric variability through graph neural networks N. R. Franco S. Fresca Filippo Tombari Andrea Manzoni AI4CE 24 16 0 03 Aug 2023
Symplectic model reduction of Hamiltonian systems using data-driven quadratic manifolds Harsh Sharma Hongliang Mu Patrick Buchfink R. Geelen Silke Glas Boris Kramer 16 28 0 24 May 2023
A graph convolutional autoencoder approach to model order reduction for parametrized PDEs F. Pichi B. Moya J. Hesthaven AI4CE 30 52 0 15 May 2023
A two stages Deep Learning Architecture for Model Reduction of Parametric Time-Dependent Problems Isabella Carla Gonnella M. Hess G. Stabile G. Rozza AI4CE 19 2 0 24 Jan 2023
Multi-fidelity surrogate modeling using long short-term memory networks Paolo Conti Mengwu Guo Andrea Manzoni J. Hesthaven AI4CE 33 48 0 05 Aug 2022
AI-enhanced iterative solvers for accelerating the solution of large scale parametrized systems Stefanos Nikolopoulos I. Kalogeris V. Papadopoulos G. Stavroulakis 16 11 0 06 Jul 2022
Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning Thomas O'Leary-Roseberry Peng Chen Umberto Villa Omar Ghattas AI4CE 29 39 0 21 Jun 2022
Multi-resolution partial differential equations preserved learning framework for spatiotemporal dynamics Xin-Yang Liu Min Zhu Lu Lu Hao Sun Jian-Xun Wang PINN AI4CE 28 45 0 09 May 2022
Active-learning-based non-intrusive Model Order Reduction Qinyu Zhuang D. Hartmann H. Bungartz Juan M Lorenzi AI4CE 14 4 0 08 Apr 2022
An AI-based Domain-Decomposition Non-Intrusive Reduced-Order Model for Extended Domains applied to Multiphase Flow in Pipes C. Heaney Zef Wolffs Jón Atli Tómasson L. Kahouadji P. Salinas A. Nicolle Omar K. Matar Ionel M. Navon N. Srinil Christopher C. Pain AI4CE 26 21 0 13 Feb 2022
Deep-HyROMnet: A deep learning-based operator approximation for hyper-reduction of nonlinear parametrized PDEs Ludovica Cicci S. Fresca Andrea Manzoni AI4CE 12 25 0 05 Feb 2022
Learning High-Dimensional Parametric Maps via Reduced Basis Adaptive Residual Networks Thomas O'Leary-Roseberry Xiaosong Du A. Chaudhuri J. Martins Karen E. Willcox Omar Ghattas 30 22 0 14 Dec 2021
Real-time simulation of parameter-dependent fluid flows through deep learning-based reduced order models S. Fresca Andrea Manzoni AI4CE 11 36 0 10 Jun 2021
A Deep Learning approach to Reduced Order Modelling of Parameter Dependent Partial Differential Equations N. R. Franco Andrea Manzoni P. Zunino 18 45 0 10 Mar 2021