Physics Guided Machine Learning for Variational Multiscale Reduced Order Modeling

Ahmed, Shady E.; San, Omer; Rasheed, Adil; Iliescu, Traian; Veneziani, Alessandro

doi:10.1137/22m1496360

Home/Archive/DOE OSTI

DOE OSTI · 2421108

Physics Guided Machine Learning for Variational Multiscale Reduced Order Modeling

Ahmed, Shady E.·San, Omer·Rasheed, Adil·Iliescu, Traian·Veneziani, Alessandro

View original source ↗Export metadata ↓

Abstract

Not provided.

Explore related subjects

Mathematics

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Ahmed, Shady E., San, Omer, Rasheed, Adil, Iliescu, Traian, Veneziani, Alessandro. 2023-06-06. Physics Guided Machine Learning for Variational Multiscale Reduced Order Modeling. https://doi.org/10.1137/22m1496360

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

DOE OSTI2025

Bayesian model selection for neural networks

Explore the source record for details and available documents.

Mathematics↗

DOE OSTI2024

Continuous and Discrete Baroclinic Modes in Continuously Varying Stratification

Not provided.

Mathematics↗

DOE OSTI2024

Global Magni4icence, or: 4G Networks

The global magnificent four theory is the homological version of a maximally supersymmetric $(8+1)$-dimensional gauge theory on a Calabi-Yau fourfold fibered over a circle. In the case of a toric fourfold we conjecture the formula for its twisted Witten index. String-theoretically we count the BPS states of a system of $D0$-$D2$-$D4$-$D6$-$D8$-branes on the Calabi-Yau fourfold in the presence of a large Neveu-Schwarz $B$-field. Mathematically, we develop the equivariant $K$-theoretic DT4 theory, by constructing the four-valent vertex with generic plane partition asymptotics. Physically, the vertex is a supersymmetric localization of a non-commutative gauge theory in $8+1$ dimensions.

Mathematics↗