UB Paderborn / Katalog / Suche / in CDI

Sortiert nach

Einfacher Volltextzugriff von (fast) überall

Installieren Sie die LibKey Nomad-Browsererweiterung und erhalten Sie von vielen Webseiten einfachen Zugriff auf lizenzierte Volltexte oder Open Access Publikationen. Mehr erfahren...

Predictive collective variable discovery with deep Bayesian models

The Journal of chemical physics, 2019-01, Vol.150 (2), p.024109-024109

2019

Volltextzugriff (PDF)

Application of Machine Learning Algorithms to Metadynamics for the Elucidation of the Binding Modes and Free Energy Landscape of Drug/Target Interactions: a Case Study

Chemistry : a European journal, 2023-11, Vol.29 (62), p.e202302375-n/a

2023

Volltextzugriff (PDF)

Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data

Journal of computational physics, 2019-10, Vol.394, p.56-81

2019

Volltextzugriff (PDF)

A novel Bayesian strategy for the identification of spatially varying material properties and model validation: an application to static elastography

International journal for numerical methods in engineering, 2012-07, Vol.91 (3), p.249-268

Koutsourelakis, Phaedon-Stelios

2012

Volltextzugriff (PDF)

Self-supervised optimization of random material microstructures in the small-data regime

npj computational materials, 2022-03, Vol.8 (1), p.1-11, Article 46

2022

Volltextzugriff (PDF)

A probabilistic, data-driven closure model for RANS simulations with aleatoric, model uncertainty

Journal of computational physics, 2024-07, Vol.508, p.112982, Article 112982

2024

Volltextzugriff (PDF)

Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems

Journal of computational physics, 2020-10, Vol.419, p.109673, Article 109673

2020

Volltextzugriff (PDF)

A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the Small Data regime

Journal of computational physics, 2019-11, Vol.397, p.108842, Article 108842

2019

Volltextzugriff (PDF)

Semi-supervised invertible neural operators for Bayesian inverse problems

Computational mechanics, 2023-09, Vol.72 (3), p.451-470, Article 451

2023

Volltextzugriff (PDF)

A probabilistic generative model for semi-supervised training of coarse-grained surrogates and enforcing physical constraints through virtual observables

Journal of computational physics, 2021-06, Vol.434, p.110218, Article 110218

2021

Volltextzugriff (PDF)

Multifidelity approaches for uncertainty quantification

Mitteilungen der Gesellschaft für Angewandte Mathematik und Mechanik, 2019-05, Vol.42 (2), p.n/a

2019

Volltextzugriff (PDF)

Predictive coarse-graining

Journal of computational physics, 2017-03, Vol.333 (C), p.49-77

2017

Volltextzugriff (PDF)

A generalized probabilistic learning approach for multi-fidelity uncertainty quantification in complex physical simulations

Computer methods in applied mechanics and engineering, 2022-10, Vol.400, p.115600, Article 115600

2022

Volltextzugriff (PDF)

Physics-Constrained, Data-Driven Discovery of Coarse-Grained Dynamics

Communications in computational physics, 2019, Vol.25 (5)

2019

Volltextzugriff (PDF)

Bayesian Model and Dimension Reduction for Uncertainty Propagation: Applications in Random Media

SIAM/ASA journal on uncertainty quantification, 2019-01, Vol.7 (1), p.292-323

2019

Volltextzugriff (PDF)

Accurate Uncertainty Quantification Using Inaccurate Computational Models

SIAM journal on scientific computing, 2009-01, Vol.31 (5), p.3274-3300

Koutsourelakis, Phaedon-Stelios

2009

Volltextzugriff (PDF)

STOCHASTIC DESIGN AND CONTROL IN RANDOM HETEROGENEOUS MATERIALS

International journal for multiscale computational engineering, 2011, Vol.9 (4), p.425-443

2011

Volltextzugriff (PDF)

Model-based structural health monitoring of naval ship hulls

Computer methods in applied mechanics and engineering, 2011-02, Vol.200 (9), p.1137-1149

2011

Volltextzugriff (PDF)

Scalable Bayesian Reduced-Order Models for Simulating High-Dimensional Multiscale Dynamical Systems

Multiscale modeling & simulation, 2011-01, Vol.9 (1), p.449-485

2011

Volltextzugriff (PDF)

Physics-Aware Neural Implicit Solvers for multiscale, parametric PDEs with applications in heterogeneous media

Computer methods in applied mechanics and engineering, 2024-12, Vol.432, p.117342, Article 117342

2024

Volltextzugriff (PDF)

Simulation of Binary Random Fields with Applications to Two-Phase Random Media

Journal of engineering mechanics, 2005-04, Vol.131 (4), p.397-412

2005

Volltextzugriff (PDF)

Bayesian inference for random field parameters with a goal-oriented quality control of the PGD forward model’s accuracy

Computational mechanics, 2022-12, Vol.70 (6), p.1189-1210

2022

Volltextzugriff (PDF)

A data‐driven model order reduction approach for Stokes flow through random porous media

Proceedings in applied mathematics and mechanics, 2018-12, Vol.18 (1), p.n/a

2018

Volltextzugriff (PDF)

A probabilistic white‐box model for PDE constrained inverse problems

Proceedings in applied mathematics and mechanics, 2018-12, Vol.18 (1), p.n/a

2018

Volltextzugriff (PDF)

BEYOND BLACK-BOXES IN BAYESIAN INVERSE PROBLEMS AND MODEL VALIDATION: APPLICATIONS IN SOLID MECHANICS OF ELASTOGRAPHY

International journal for uncertainty quantification, 2018, Vol.8 (5), p.447-482

2018

Volltextzugriff (PDF)

1 – 25 von 28

Suche als RSS-Feed

Suchergebnisse filtern

Filter anzeigen

Nur zeigen

Geprüft (Peer-Review)
(25)
Mit Volltextzugriff
(23)

Publikationsform

Artikel
(27)
Dissertationen
(1)

Sprache

Englisch
(28)

Erscheinungsjahr

n.n

Thema

Science & Technology
(21)
Physical Sciences
(20)
Technology
(15)
Physics
(12)
Mathematics
(11)
Engineering
(9)
Mathematics, Interdisciplinary Applications
(9)
Physics, Mathematical
(9)
Uncertainty Quantification
(9)
Bayesian
(7)
Machine Learning
(7)
Uncertainty
(7)
Bayesian Analysis
(6)
Computer Science
(6)
Computer Science, Interdisciplinary Applications
(6)
Computational Physics
(5)
Engineering, Multidisciplinary
(5)
Computer Simulation
(4)
Inverse Problems
(4)
Mathematical Models
(4)

15 weitere anzeigen...

Weniger anzeigen...

Neu hinzugefügt

... seit 30 Tagen
(1)

Menü

Weitere Dienste

Einstellungen

Predictive collective variable discovery with deep Bayesian models

Application of Machine Learning Algorithms to Metadynamics for the Elucidation of the Binding Modes and Free Energy Landscape of Drug/Target Interactions: a Case Study

Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data

A novel Bayesian strategy for the identification of spatially varying material properties and model validation: an application to static elastography

Self-supervised optimization of random material microstructures in the small-data regime

A probabilistic, data-driven closure model for RANS simulations with aleatoric, model uncertainty

Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems

A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the Small Data regime

Semi-supervised invertible neural operators for Bayesian inverse problems

A probabilistic generative model for semi-supervised training of coarse-grained surrogates and enforcing physical constraints through virtual observables

Multifidelity approaches for uncertainty quantification

Predictive coarse-graining

A generalized probabilistic learning approach for multi-fidelity uncertainty quantification in complex physical simulations

Physics-Constrained, Data-Driven Discovery of Coarse-Grained Dynamics

Bayesian Model and Dimension Reduction for Uncertainty Propagation: Applications in Random Media

Accurate Uncertainty Quantification Using Inaccurate Computational Models

STOCHASTIC DESIGN AND CONTROL IN RANDOM HETEROGENEOUS MATERIALS

Model-based structural health monitoring of naval ship hulls

Scalable Bayesian Reduced-Order Models for Simulating High-Dimensional Multiscale Dynamical Systems

Physics-Aware Neural Implicit Solvers for multiscale, parametric PDEs with applications in heterogeneous media

Simulation of Binary Random Fields with Applications to Two-Phase Random Media

Bayesian inference for random field parameters with a goal-oriented quality control of the PGD forward model’s accuracy

A data‐driven model order reduction approach for Stokes flow through random porous media

A probabilistic white‐box model for PDE constrained inverse problems

BEYOND BLACK-BOXES IN BAYESIAN INVERSE PROBLEMS AND MODEL VALIDATION: APPLICATIONS IN SOLID MECHANICS OF ELASTOGRAPHY

Suchergebnisse filtern