Copertina

On latent dynamics learning in nonlinear reduced order modeling

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Farenga, Nicola, Fresca, Stefania, Brivio, Simone, 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

PTPI-DL-ROMs: pre-trained physics-informed deep learning-based reduced order models for nonlinear parametrized PDEs
di: Brivio, Simone, et al.
Pubblicazione: (2024)

Handling geometrical variability in nonlinear reduced order modeling through Continuous Geometry-Aware DL-ROMs
di: Brivio, Simone, 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)

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

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

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

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)

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

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

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

Sequential decoder training for improved latent space dynamics identification
di: Anderson, William, et al.
Pubblicazione: (2025)

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)

Learning reduced-order Quadratic-Linear models in Process Engineering using Operator Inference
di: Gosea, Ion Victor, et al.
Pubblicazione: (2024)

mLaSDI: Multi-stage latent space dynamics identification
di: Anderson, William, et al.
Pubblicazione: (2025)

Learning cardiac activation and repolarization times with operator learning
di: Centofanti, Edoardo, et al.
Pubblicazione: (2025)

A model learning framework for inferring the dynamics of transmission rate depending on exogenous variables for epidemic forecasts
di: Ziarelli, Giovanni, et al.
Pubblicazione: (2024)

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

Gaussian process learning of nonlinear dynamics
di: Ye, Dongwei, et al.
Pubblicazione: (2023)

Patient-specific prediction of glioblastoma growth via reduced order modeling and neural networks
di: Cerrone, D., et al.
Pubblicazione: (2024)

Physically consistent predictive reduced-order modeling by enhancing Operator Inference with state constraints
di: Kim, Hyeonghun, et al.
Pubblicazione: (2025)

Physics-informed active learning with simultaneous weak-form latent space dynamics identification
di: He, Xiaolong, et al.
Pubblicazione: (2024)

GFN: A graph feedforward network for resolution-invariant reduced operator learning in multifidelity applications
di: Morrison, Oisín M., et al.
Pubblicazione: (2024)

Flow-based Bayesian filtering for high-dimensional nonlinear stochastic dynamical systems
di: Wang, Xintong, et al.
Pubblicazione: (2025)

Leveraging time and parameters for nonlinear model reduction methods
di: Glas, Silke, et al.
Pubblicazione: (2025)

A fast neural hybrid Newton solver adapted to implicit methods for nonlinear dynamics
di: Jin, Tianyu, et al.
Pubblicazione: (2024)

Domain Decomposition-based coupling of Operator Inference reduced order models via the Schwarz alternating method
di: Moore, Ian, et al.
Pubblicazione: (2024)

Structure-preserving Lift & Learn: Scientific machine learning for nonlinear conservative partial differential equations
di: Sharma, Harsh, et al.
Pubblicazione: (2025)

Provable Emergence of Deep Neural Collapse and Low-Rank Bias in $L^2$-Regularized Nonlinear Networks
di: Zangrando, Emanuele, et al.
Pubblicazione: (2024)

The role of interface boundary conditions and sampling strategies for Schwarz-based coupling of projection-based reduced order models
di: Wentland, Christopher R., et al.
Pubblicazione: (2024)

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

Neural active manifolds: nonlinear dimensionality reduction for uncertainty quantification
di: Zanoni, Andrea, et al.
Pubblicazione: (2024)

Nonlinear model reduction for operator learning
di: Eivazi, Hamidreza, et al.
Pubblicazione: (2024)

Solving and learning advective multiscale Darcian dynamics with the Neural Basis Method
di: Wang, Yuhe, et al.
Pubblicazione: (2026)

Explainable Deep Learning-based Classification of Wolff-Parkinson-White Electrocardiographic Signals
di: Ragonesi, Alice, et al.
Pubblicazione: (2025)

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

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

Probabilistic operator learning: generative modeling and uncertainty quantification for foundation models of differential equations
di: Zhang, Benjamin J., et al.
Pubblicazione: (2025)

CoLoRA: Continuous low-rank adaptation for reduced implicit neural modeling of parameterized partial differential equations
di: Berman, Jules, et al.
Pubblicazione: (2024)