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Found 8 papers, 2 papers with code
Application of probabilistic modeling and automated machine learning framework for high-dimensional stress field
Modern computational methods, involving highly sophisticated mathematical formulations, enable several tasks like modeling complex physical phenomenon, predicting key properties and design optimization.
Interpretable Uncertainty Quantification in AI for HEP
Estimating uncertainty is at the core of performing scientific measurements in HEP: a measurement is not useful without an estimate of its uncertainty.
Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning
This reconstruction problem is especially difficult when sensors are sparsely positioned in a seemingly random or unorganized manner, which is often encountered in a range of scientific and engineering problems.
Peculiar Velocity Estimation from Kinetic SZ Effect using Deep Neural Networks
The Sunyaev-Zel'dolvich (SZ) effect is expected to be instrumental in measuring velocities of distant clusters in near future telescope surveys.
Cosmology and Nongalactic Astrophysics
Beyond the Hubble Sequence -- Exploring Galaxy Morphology with Unsupervised Machine Learning
Based on this, the main result in this work is how well our unsupervised method matches visual classifications and physical properties, as well as providing an independent classification that is more physically meaningful than any visually based ones.
Astrophysics of Galaxies
Latent-space time evolution of non-intrusive reduced-order models using Gaussian process emulation
We assess the viability of this algorithm for an advection-dominated system given by the inviscid shallow water equations.
Probabilistic neural networks for fluid flow surrogate modeling and data recovery
We consider the use of probabilistic neural networks for fluid flow surrogate modeling and data recovery.
Fluid Dynamics
A Modular Deep Learning Pipeline for Galaxy-Scale Strong Gravitational Lens Detection and Modeling
Upcoming large astronomical surveys are expected to capture an unprecedented number of strong gravitational lensing systems.
