Reduced Variance Random Batch Methods for Nonlocal PDEs

to related objects

Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: 1 Online-Ressource.

Language: Englisch

Bibliographic citation: Reduced Variance Random Batch Methods for Nonlocal PDEs ; volume:191 ; number:1 ; day:9 ; month:5 ; year:2024 ; pages:1-23 ; date:6.2024
Acta applicandae mathematicae ; 191, Heft 1 (9.5.2024), 1-23, 6.2024

Creator: Pareschi, Lorenzo
Zanella, Mattia

Contributor: SpringerLink (Online service)

DOI: 10.1007/s10440-024-00656-z

URN: urn:nbn:de:101:1-2409110948078.922550111399

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:33 AM CEST

Data provider

This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.

Show original at data provider

Associated

Pareschi, Lorenzo
Zanella, Mattia
SpringerLink (Online service)

Other Objects (12)

Nonlocal Graph-PDEs and Riemannian Gradient Flows for Image Labeling

Accelerating variance-reduced stochastic gradient methods

Deep learning approximations for non-local nonlinear PDEs with Neumann boundary conditions

Variance reduced Value at Risk Monte-Carlo simulations

Reduced-variance orientational distribution functions from torque sampling

Reduced basis methods for quasilinear PDEs and applications to electrical machines

Weighted reduced basis methods for parabolic PDEs with random input data

$Fine bounds for best constants of fractional subcritical Sobolev embeddings and applications to nonlocal PDEs$

Fine bounds for best constants of fractional subcritical Sobolev embeddings and applications to nonlocal PDEs

Spectrally accurate fully discrete schemes for some nonlocal and nonlinear integrable PDEs via explicit formulas

Variance reduced Monte-Carlo Simulations of Stochastic Differential Equations

Graffiti | Streetart

bAtch BATCH

Bitcoin: a new proof-of-work system with reduced variance

Nonlocal Graph-PDEs and Riemannian Gradient Flows for Image Labeling

Accelerating variance-reduced stochastic gradient methods

Deep learning approximations for non-local nonlinear PDEs with Neumann boundary conditions

Variance reduced Value at Risk Monte-Carlo simulations

Reduced-variance orientational distribution functions from torque sampling

Reduced basis methods for quasilinear PDEs and applications to electrical machines

Weighted reduced basis methods for parabolic PDEs with random input data

$Fine bounds for best constants of fractional subcritical Sobolev embeddings and applications to nonlocal PDEs$

Fine bounds for best constants of fractional subcritical Sobolev embeddings and applications to nonlocal PDEs

Spectrally accurate fully discrete schemes for some nonlocal and nonlinear integrable PDEs via explicit formulas

Variance reduced Monte-Carlo Simulations of Stochastic Differential Equations

Graffiti | Streetart

bAtch BATCH

Bitcoin: a new proof-of-work system with reduced variance

Nonlocal Graph-PDEs and Riemannian Gradient Flows for Image Labeling

Accelerating variance-reduced stochastic gradient methods

Deep learning approximations for non-local nonlinear PDEs with Neumann boundary conditions

Variance reduced Value at Risk Monte-Carlo simulations

Reduced-variance orientational distribution functions from torque sampling

Reduced basis methods for quasilinear PDEs and applications to electrical machines

Weighted reduced basis methods for parabolic PDEs with random input data

$Fine bounds for best constants of fractional subcritical Sobolev embeddings and applications to nonlocal PDEs$

Fine bounds for best constants of fractional subcritical Sobolev embeddings and applications to nonlocal PDEs

Spectrally accurate fully discrete schemes for some nonlocal and nonlinear integrable PDEs via explicit formulas

Variance reduced Monte-Carlo Simulations of Stochastic Differential Equations

Graffiti | Streetart

bAtch BATCH

Bitcoin: a new proof-of-work system with reduced variance

Cultural heritage institutions wishing to register will find more information here.

Fields marked * need to be filled in.

Username*

Please enter your username

Email*

Please enter your email address

Please do not fill this field

First name

Last name

Password*

Please enter your password

Confirm password*

Please enter the same password

I have read the terms of use and the privacy policy for the collection of personal data and accept them. *

This field is required.

I would like to subscribe to the newsletter of the Deutsche Digitale Bibliothek. See newsletter subscription info.

Account created

Your "My DDB" account has been successfully created. Before you can log in to your account, you must click the confirmation link in the message we just sent to the email address you provided.

Reduced Variance Random Batch Methods for Nonlocal PDEs

Object Details

References and Relationships

Contributors, Places and Time

Further information

Data provider

Associated

Other Objects (12)

Nonlocal Graph-PDEs and Riemannian Gradient Flows for Image Labeling

Accelerating variance-reduced stochastic gradient methods

Deep learning approximations for non-local nonlinear PDEs with Neumann boundary conditions

Variance reduced Value at Risk Monte-Carlo simulations

Reduced-variance orientational distribution functions from torque sampling

Reduced basis methods for quasilinear PDEs and applications to electrical machines

Weighted reduced basis methods for parabolic PDEs with random input data

Fine bounds for best constants of fractional subcritical Sobolev embeddings and applications to nonlocal PDEs

Spectrally accurate fully discrete schemes for some nonlocal and nonlinear integrable PDEs via explicit formulas

Variance reduced Monte-Carlo Simulations of Stochastic Differential Equations

bAtch BATCH

Bitcoin: a new proof-of-work system with reduced variance

Nonlocal Graph-PDEs and Riemannian Gradient Flows for Image Labeling

Accelerating variance-reduced stochastic gradient methods

Deep learning approximations for non-local nonlinear PDEs with Neumann boundary conditions

Variance reduced Value at Risk Monte-Carlo simulations

Reduced-variance orientational distribution functions from torque sampling

Reduced basis methods for quasilinear PDEs and applications to electrical machines

Weighted reduced basis methods for parabolic PDEs with random input data

Fine bounds for best constants of fractional subcritical Sobolev embeddings and applications to nonlocal PDEs

Spectrally accurate fully discrete schemes for some nonlocal and nonlinear integrable PDEs via explicit formulas

Variance reduced Monte-Carlo Simulations of Stochastic Differential Equations

bAtch BATCH

Bitcoin: a new proof-of-work system with reduced variance

Nonlocal Graph-PDEs and Riemannian Gradient Flows for Image Labeling

Accelerating variance-reduced stochastic gradient methods

Deep learning approximations for non-local nonlinear PDEs with Neumann boundary conditions

Variance reduced Value at Risk Monte-Carlo simulations

Reduced-variance orientational distribution functions from torque sampling

Reduced basis methods for quasilinear PDEs and applications to electrical machines

Weighted reduced basis methods for parabolic PDEs with random input data

Fine bounds for best constants of fractional subcritical Sobolev embeddings and applications to nonlocal PDEs

Spectrally accurate fully discrete schemes for some nonlocal and nonlinear integrable PDEs via explicit formulas

Variance reduced Monte-Carlo Simulations of Stochastic Differential Equations

bAtch BATCH

Bitcoin: a new proof-of-work system with reduced variance

Related objects

Reset password