:: Library Catalog

Ata uhi

I tiakina i:

Ngā taipitopito rārangi puna kōrero
Ngā kaituhi matua:	Ortali, Giulio, Demo, Nicola, Rozza, Gianluigi
Hōputu:	Preprint
I whakaputaina:	2020
Ngā marau:	Numerical Analysis
Urunga tuihono:	https://arxiv.org/abs/2012.01989
Ngā Tūtohu:	Tāpirihia he Tūtohu Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!

Ngā tūemi rite

An efficient computational framework for naval shape design and optimization problems by means of data-driven reduced order modeling techniques
mā: Demo, Nicola, me ētahi atu.
I whakaputaina: (2020)

An extended physics informed neural network for preliminary analysis of parametric optimal control problems
mā: Demo, Nicola, me ētahi atu.
I whakaputaina: (2021)

Towards a machine learning pipeline in reduced order modelling for inverse problems: neural networks for boundary parametrization, dimensionality reduction and solution manifold approximation
mā: Ivagnes, Anna, me ētahi atu.
I whakaputaina: (2022)

A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks
mā: Meneghetti, Laura, me ētahi atu.
I whakaputaina: (2022)

Generative Adversarial Reduced Order Modelling
mā: Coscia, Dario, me ētahi atu.
I whakaputaina: (2023)

A Shape Optimization Pipeline for Marine Propellers by means of Reduced Order Modeling Techniques
mā: Ivagnes, Anna, me ētahi atu.
I whakaputaina: (2023)

Non-intrusive model reduction of advection-dominated hyperbolic problems using neural network shift augmented manifold transformation
mā: Gowrachari, Harshith, me ētahi atu.
I whakaputaina: (2024)

A Dimensionality Reduction Approach for Convolutional Neural Networks
mā: Meneghetti, Laura, me ētahi atu.
I whakaputaina: (2021)

Data-driven Discovery of Delay Differential Equations with Discrete Delays
mā: Pecile, Alessandro, me ētahi atu.
I whakaputaina: (2024)

Kinetic data-driven approach to turbulence subgrid modeling
mā: Ortali, Giulio, me ētahi atu.
I whakaputaina: (2024)

A Dynamic Mode Decomposition Extension for the Forecasting of Parametric Dynamical Systems
mā: Andreuzzi, Francesco, me ētahi atu.
I whakaputaina: (2021)

Machine Learning-based quadratic closures for non-intrusive Reduced Order Models
mā: Codega, Gabriele, me ētahi atu.
I whakaputaina: (2025)

Stabilized POD Reduced Order Models for convection-dominated incompressible flows
mā: Siena, Pierfrancesco, me ētahi atu.
I whakaputaina: (2024)

An optimisation-based domain-decomposition reduced order model for parameter-dependent non-stationary fluid dynamics problems
mā: Prusak, Ivan, me ētahi atu.
I whakaputaina: (2023)

Model Order Reduction of Cerebrovascular Hemodynamics Using POD_Galerkin and Reservoir Computing_based Approach
mā: Halder, Rahul, me ētahi atu.
I whakaputaina: (2026)

Computational study of numerical flux schemes for mesoscale atmospheric flows in a Finite Volume framework
mā: Clinco, Nicola, me ētahi atu.
I whakaputaina: (2024)

A reduced-order model for segregated fluid-structure interaction solvers based on an ALE approach
mā: Ngan, Valentin Nkana, me ētahi atu.
I whakaputaina: (2023)

Nonlinear reduction strategies for data compression: a comprehensive comparison from diffusion to advection problems
mā: Gonnella, Isabella Carla, me ētahi atu.
I whakaputaina: (2025)

A stochastic perturbation approach to nonlinear bifurcating problems
mā: Gonnella, Isabella Carla, me ētahi atu.
I whakaputaina: (2024)

Data-driven Closure Strategies for Parametrized Reduced Order Models via Deep Operator Networks
mā: Ivagnes, Anna, me ētahi atu.
I whakaputaina: (2025)

A LSTM-enhanced surrogate model to simulate the dynamics of particle-laden fluid systems
mā: Hajisharifi, Arash, me ētahi atu.
I whakaputaina: (2024)

A hybrid reduced-order model for segregated fluid-structure interaction solvers in an ALE approach at high Reynolds number
mā: Ngan, Valentin Nkana, me ētahi atu.
I whakaputaina: (2024)

Generative Models for Parameter Space Reduction applied to Reduced Order Modelling
mā: Padula, Guglielmo, me ētahi atu.
I whakaputaina: (2025)

Randomized Proper Orthogonal Decomposition for data-driven reduced order modeling of a two-layer quasi-geostrophic ocean model
mā: Besabe, Lander, me ētahi atu.
I whakaputaina: (2025)

A local approach to parameter space reduction for regression and classification tasks
mā: Romor, Francesco, me ētahi atu.
I whakaputaina: (2021)

Data-Driven POD-Galerkin Reduced Order Model for Turbulent Flows
mā: Hijazi, Saddam, me ētahi atu.
I whakaputaina: (2019)

Data-driven reduced order modeling of a two-layer quasi-geostrophic ocean model
mā: Besabe, Lander, me ētahi atu.
I whakaputaina: (2024)

On an adjoint-based numerical approach for time-dependent optimal control problems of biomedical interest
mā: Mirzaiyan, Zahra, me ētahi atu.
I whakaputaina: (2025)

An efficient hyper reduced-order model for segregated solvers for geometrical parametrization problems
mā: Ngan, Valentin Nkana, me ētahi atu.
I whakaputaina: (2026)

Model Reduction for Transport-Dominated Problems via Cross-Correlation Based Snapshot Registration
mā: Gowrachari, Harshith, me ētahi atu.
I whakaputaina: (2025)

Coupling Physics Informed Neural Networks with External Solvers
mā: Halder, Rahul, me ētahi atu.
I whakaputaina: (2025)

Bifurcation curve detection with deflation for multiparametric PDEs
mā: Kumar, Nitin, me ētahi atu.
I whakaputaina: (2026)

Reinforcement Learning-Based Filters for Convection-Dominated Flows: Reference-Free and Reference-Guided Training
mā: Ivagnes, Anna, me ētahi atu.
I whakaputaina: (2026)

A brief review of Reduced Order Models using intrusive and non-intrusive techniques
mā: Padula, Guglielmo, me ētahi atu.
I whakaputaina: (2024)

Data-driven parameterization refinement for the structural optimization of cruise ship hulls
mā: Fabris, Lorenzo, me ētahi atu.
I whakaputaina: (2024)

A time-adaptive algorithm for pressure dominated flows: a heuristic estimator
mā: Prusak, Ivan, me ētahi atu.
I whakaputaina: (2024)

Assessment of URANS and LES Methods in Predicting Wake Shed Behind a Vertical Axis Wind Turbine
mā: Sheidani, Armin, me ētahi atu.
I whakaputaina: (2022)

Parametric Intrusive Reduced Order Models enhanced with Machine Learning Correction Terms
mā: Ivagnes, Anna, me ētahi atu.
I whakaputaina: (2024)

Physics Informed Neural Network Framework for Unsteady Discretized Reduced Order System
mā: Halder, Rahul, me ētahi atu.
I whakaputaina: (2023)

Latent Dynamics Graph Convolutional Networks for model order reduction of parameterized time-dependent PDEs
mā: Tomada, Lorenzo, me ētahi atu.
I whakaputaina: (2026)