Copertina

Deep learning enhanced cost-aware multi-fidelity uncertainty quantification of a computational model for radiotherapy

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Vitullo, Piermario, Franco, Nicola Rares, Zunino, Paolo
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Numerical Analysis
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

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

Mathematical modeling and sensitivity analysis of hypoxia-activated drugs
di: Coclite, Alessandro, et al.
Pubblicazione: (2025)

Physics-Informed Learning of Microvascular Flow Models using Graph Neural Networks
di: Botta, Paolo, et al.
Pubblicazione: (2025)

Measurability and continuity of parametric low-rank approximation in Hilbert spaces: linear operators and random variables
di: Franco, Nicola Rares
Pubblicazione: (2024)

Numerical Solution of Mixed-Dimensional PDEs Using a Neural Preconditioner
di: Dimola, Nunzio, 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 set based operator learning with uncertainty quantification
di: Ma, Lei, et al.
Pubblicazione: (2025)

Latent representation learning based model correction and uncertainty quantification for PDEs
di: Zhou, Wenwen, et al.
Pubblicazione: (2026)

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

Reduced Lagrange multiplier approach for non-matching coupling of mixed-dimensional domains
di: Heltai, Luca, et al.
Pubblicazione: (2023)

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

Vanquishing the computational cost of passive gamma emission tomography simulations: a physics-aware reduced order modeling approach
di: Cavallini, Nicola, et al.
Pubblicazione: (2023)

On uncertainty quantification of eigenvalues and eigenspaces with higher multiplicity
di: Dölz, Jürgen, et al.
Pubblicazione: (2022)

Application of Deep Learning Reduced-Order Modeling for Single-Phase Flow in Faulted Porous Media
di: Ballini, Enrico, et al.
Pubblicazione: (2024)

A conditional normalizing flow for domain decomposed uncertainty quantification
di: Li, Sen, et al.
Pubblicazione: (2024)

Towards uncertainty quantification of a model for cancer-on-chip experiments
di: Bertoluzza, Silvia, et al.
Pubblicazione: (2026)

On a class of multi-fidelity methods for the semiclassical Schrödinger equation with uncertainties
di: Lin, Yiwen, et al.
Pubblicazione: (2024)

Accelerating seismic inversion and uncertainty quantification with efficient high-rank Hessian approximations
di: Hu, Mathew, et al.
Pubblicazione: (2025)

Framework for uncertainty quantification of wave-structure interaction in a flume
di: Luo, Xiaoyuan, et al.
Pubblicazione: (2024)

Data-driven dimensionally decomposed generalized polynomial chaos expansion for forward uncertainty quantification
di: Choi, Hojun, et al.
Pubblicazione: (2025)

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

LDDMM stochastic interpolants: an application to domain uncertainty quantification in hemodynamics
di: Katz, Sarah, et al.
Pubblicazione: (2026)

Improvements on uncertainty quantification with variational autoencoders
di: Tonini, Andrea, et al.
Pubblicazione: (2025)

A short tour of operator learning theory: Convergence rates, statistical limits, and open questions
di: Brugiapaglia, Simone, et al.
Pubblicazione: (2026)

A polynomial chaos approach for uncertainty quantification of Monte Carlo transport codes
di: Geraci, Gianluca, et al.
Pubblicazione: (2024)

Model reduction of parametric ordinary differential equations via autoencoders: representation properties and convergence analysis
di: Ballini, Enrico, et al.
Pubblicazione: (2025)

A comparative analysis of metamodels for 0D cardiovascular models, and pipeline for sensitivity analysis, parameter estimation, and uncertainty quantification
di: Hanna, John M., et al.
Pubblicazione: (2024)

Efficient forward and inverse uncertainty quantification for dynamical systems based on dimension reduction and Kriging surrogate modeling in functional space
di: Song, Zhouzhou, et al.
Pubblicazione: (2025)

Data driven learning to enhance a kinetic model of distressed crowd dynamics
di: Kim, Daewa, et al.
Pubblicazione: (2024)

AutoVARP -- a framework for automated reproducible inducibility testing in computational models of cardiac electrophysiology
di: Seghetti, Paolo, et al.
Pubblicazione: (2025)

Structure and asymptotic preserving deep neural surrogates for uncertainty quantification in multiscale kinetic equations
di: Chen, Wei, et al.
Pubblicazione: (2025)

A multi-fidelity adaptive dynamical low-rank based optimization algorithm for fission criticality problems
di: Scalone, C., et al.
Pubblicazione: (2024)

Sparse discovery of differential equations based on multi-fidelity Gaussian process
di: Meng, Yuhuang, et al.
Pubblicazione: (2024)

Noise-robust multi-fidelity surrogate modelling for parametric partial differential equations
di: Kent, Benjamin M., et al.
Pubblicazione: (2025)

A multi-mesh approach for accurate computation of multi-target functionals in aerodynamics design
di: Hu, Guanghui, et al.
Pubblicazione: (2024)

An efficient computational framework for naval shape design and optimization problems by means of data-driven reduced order modeling techniques
di: Demo, Nicola, et al.
Pubblicazione: (2020)

Spatially-varying meshless approximation method for enhanced computational efficiency
di: Jančič, Mitja, et al.
Pubblicazione: (2023)

GLENN: Neural network-enhanced computation of Ginzburg-Landau energy minimizers
di: Crocoll, Michael, et al.
Pubblicazione: (2026)

A two-step surrogate method for sequential uncertainty quantification in high-dimensional inverse problems
di: Yang, Ningxin, et al.
Pubblicazione: (2024)

A DeepLagrangian method for learning and generating aggregation patterns in multi-dimensional Keller-Segel chemotaxis systems
di: Feng, Yani, et al.
Pubblicazione: (2025)