Copertina

PTPI-DL-ROMs: pre-trained physics-informed deep learning-based reduced order models for nonlinear parametrized PDEs

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Brivio, Simone, Fresca, Stefania, Manzoni, Andrea
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Numerical Analysis Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Handling geometrical variability in nonlinear reduced order modeling through Continuous Geometry-Aware DL-ROMs
di: Brivio, Simone, et al.
Pubblicazione: (2024)

On latent dynamics learning in nonlinear reduced order modeling
di: Farenga, Nicola, et al.
Pubblicazione: (2024)

Real-time optimal control of high-dimensional parametrized systems by deep learning-based reduced order models
di: Tomasetto, Matteo, et al.
Pubblicazione: (2024)

Latent feedback control of distributed systems in multiple scenarios through deep learning-based reduced order models
di: Tomasetto, Matteo, et al.
Pubblicazione: (2024)

Statistical closure modeling for reduced-order models of stationary systems by the ROMES method
di: Pagani, Stefano, et al.
Pubblicazione: (2019)

Error analysis for finite element operator learning methods for solving parametric second-order elliptic PDEs
di: Hong, Youngjoon, et al.
Pubblicazione: (2024)

Low-rank cross approximation of function-valued tensors for reduced-order modeling of parametric PDEs
di: Budzinskiy, Stanislav, et al.
Pubblicazione: (2025)

Physics-informed reduced order model with conditional neural fields
di: Kim, Minji, et al.
Pubblicazione: (2024)

Multi-Level Monte Carlo Training of Neural Operators
di: Rowbottom, James, et al.
Pubblicazione: (2025)

Towards a machine learning pipeline in reduced order modelling for inverse problems: neural networks for boundary parametrization, dimensionality reduction and solution manifold approximation
di: Ivagnes, Anna, et al.
Pubblicazione: (2022)

A practical existence theorem for reduced order models based on convolutional autoencoders
di: Franco, Nicola Rares, et al.
Pubblicazione: (2024)

Algorithmically Designed Artificial Neural Networks (ADANNs): Higher order deep operator learning for parametric partial differential equations
di: Jentzen, Arnulf, et al.
Pubblicazione: (2023)

Learning physics-based reduced-order models from data using nonlinear manifolds
di: Geelen, Rudy, et al.
Pubblicazione: (2023)

An efficient hyper reduced-order model for segregated solvers for geometrical parametrization problems
di: Ngan, Valentin Nkana, et al.
Pubblicazione: (2026)

Adaptive digital twins for predictive decision-making: Online Bayesian learning of transition dynamics
di: Varetti, Eugenio, et al.
Pubblicazione: (2025)

Sequential-in-time training of nonlinear parametrizations for solving time-dependent partial differential equations
di: Zhang, Huan, et al.
Pubblicazione: (2024)

Multi-Fidelity Delayed Acceptance: hierarchical MCMC sampling for Bayesian inverse problems combining multiple solvers through deep neural networks
di: Zacchei, Filippo, et al.
Pubblicazione: (2025)

VS-PINN: A fast and efficient training of physics-informed neural networks using variable-scaling methods for solving PDEs with stiff behavior
di: Ko, Seungchan, et al.
Pubblicazione: (2024)

A nonlinear extension of parametric model embedding for dimensionality reduction in parametric shape design
di: Serani, Andrea, et al.
Pubblicazione: (2026)

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications
di: Williams, Emily, et al.
Pubblicazione: (2024)

A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder
di: Kim, Youngkyu, et al.
Pubblicazione: (2020)

An extended physics informed neural network for preliminary analysis of parametric optimal control problems
di: Demo, Nicola, et al.
Pubblicazione: (2021)

GridapROMs.jl: Efficient reduced order modelling in the Julia programming language
di: Mueller, Nicholas, et al.
Pubblicazione: (2025)

Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective
di: Brivio, Simone, et al.
Pubblicazione: (2025)

Kolmogorov Arnold Informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov Arnold Networks
di: Wang, Yizheng, et al.
Pubblicazione: (2024)

An adjoint method for training data-driven reduced-order models
di: Liu, Donglin, et al.
Pubblicazione: (2026)

Resolution invariant deep operator network for PDEs with complex geometries
di: Huang, Jianguo, et al.
Pubblicazione: (2024)

Coupling the reduced-order model and the generative model for an importance sampling estimator
di: Wan, Xiaoliang, et al.
Pubblicazione: (2019)

Full error analysis of the random deep splitting method for nonlinear parabolic PDEs and PIDEs
di: Neufeld, Ariel, et al.
Pubblicazione: (2024)

Latent Dynamics Graph Convolutional Networks for model order reduction of parameterized time-dependent PDEs
di: Tomada, Lorenzo, et al.
Pubblicazione: (2026)

Structure-preserving learning for multi-symplectic PDEs
di: Yıldız, Süleyman, et al.
Pubblicazione: (2024)

Neural Measures for learning distributions of Random PDEs
di: Arampatzis, Georgios, et al.
Pubblicazione: (2025)

A decomposition-based robust training of physics-informed neural networks for nearly incompressible linear elasticity
di: Dick, Josef, et al.
Pubblicazione: (2025)

A deformation-based framework for learning solution mappings of PDEs defined on varying domains
di: Xiao, Shanshan, et al.
Pubblicazione: (2024)

A reduced-order model for parametrized Optimal Transport problems
di: Bonnet-Weill, Elise, et al.
Pubblicazione: (2026)

Deep orthogonal decomposition: a continuously adaptive data-driven approach to model order reduction
di: Franco, Nicola Rares, et al.
Pubblicazione: (2024)

Deep learning-based reduced-order methods for fast transient dynamics
di: Cracco, Martina, et al.
Pubblicazione: (2022)

Long-term simulation of physical and mechanical behaviors using curriculum-transfer-learning based physics-informed neural networks
di: Guo, Yuan, et al.
Pubblicazione: (2025)

Active learning for data-driven reduced models of parametric differential systems with Bayesian operator inference
di: McQuarrie, Shane A., et al.
Pubblicazione: (2025)

Domain decomposition architectures and Gauss-Newton training for physics-informed neural networks
di: Heinlein, Alexander, et al.
Pubblicazione: (2025)