Author of the publication

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models.

G. Padmanabha, J. Fuhg, C. Safta, R. Jones, and N. Bouklas. CoRR, (2024)

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed. You can also use the button next to the name to display some publications already assigned to the person.

Joan Safta

Cosmin Pocanschi

Cosmin Arad

Cosmin Lupulescu

Cosmin Contiu

Other publications of authors with the same name

Uncertainty Quantification in the Presence of Limited Climate Model Data with Discontinuities.K. Sargsyan, C. Safta, B. Debusschere, and H. Najm. ICDM Workshops, page 241-247. IEEE Computer Society, (2009)A second-order coupled immersed boundary-SAMR construction for chemically reacting flow over a heat-conducting Cartesian grid-conforming solid.K. Kedia, C. Safta, J. Ray, H. Najm, and A. Ghoniem. J. Comput. Phys., (2014)A switching Kalman filter approach to online mitigation and correction of sensor corruption for inertial navigation.A. Mustaev, N. Galioto, M. Boler, J. Jakeman, C. Safta, and A. Gorodetsky. CoRR, (2024)Entropy-based closure for probabilistic learning on manifolds.C. Soize, R. Ghanem, C. Safta, X. Huan, Z. Vane, J. Oefelein, G. Lacaze, H. Najm, Q. Tang, and X. Chen. J. Comput. Phys., (2019)Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models.G. Padmanabha, J. Fuhg, C. Safta, R. Jones, and N. Bouklas. CoRR, (2024)Enhancing statistical moment calculations for stochastic Galerkin solutions with Monte Carlo techniques.K. Chowdhary, C. Safta, and H. Najm. J. Comput. Phys., (2018)TChem: A performance portable parallel software toolkit for complex kinetic mechanisms.K. Kim, O. Díaz-Ibarra, H. Najm, J. Zádor, and C. Safta. Comput. Phys. Commun., (April 2023)Accelerating Phase Field Simulations Through a Hybrid Adaptive Fourier Neural Operator with U-Net Backbone.C. Bonneville, N. Bieberdorf, A. Hegde, M. Asta, H. Najm, L. Capolungo, and C. Safta. CoRR, (2024)ULFM-MPI Implementation of a Resilient Task-Based Partial Differential Equations Preconditioner.F. Rizzi, K. Morris, K. Sargsyan, P. Mycek, C. Safta, B. Debusschere, O. Maître, and O. Knio. FTXS@HPDC, page 19-26. ACM, (2016)Compressive sensing adaptation for polynomial chaos expansions.P. Tsilifis, X. Huan, C. Safta, K. Sargsyan, G. Lacaze, J. Oefelein, H. Najm, and R. Ghanem. J. Comput. Phys., (2019)

BibSonomy

Disambiguation of "Safta, Cosmin"

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models.

Please choose a person to relate this publication to

Joan Safta

Cosmin Pocanschi

Cosmin Arad

Cosmin Lupulescu

Cosmin Contiu

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Safta, Cosmin"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models.

Please choose a person to relate this publication to

Joan Safta

Cosmin Pocanschi

Cosmin Arad

Cosmin Lupulescu

Cosmin Contiu

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models.