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Found 6 papers, 3 papers with code
The CAMELS project: Expanding the galaxy formation model space with new ASTRID and 28-parameter TNG and SIMBA suites
We present CAMELS-ASTRID, the third suite of hydrodynamical simulations in the Cosmology and Astrophysics with MachinE Learning (CAMELS) project, along with new simulation sets that extend the model parameter space based on the previous frameworks of CAMELS-TNG and CAMELS-SIMBA, to provide broader training sets and testing grounds for machine-learning algorithms designed for cosmological studies.
AI-assisted super-resolution cosmological simulations II: Halo substructures, velocities and higher order statistics
In this work, we expand and test the capabilities of our recently developed super-resolution (SR) model to generate high-resolution (HR) realizations of the full phase-space matter distribution, including both displacement and velocity, from computationally cheap low-resolution (LR) cosmological N-body simulations.
AI-assisted super-resolution cosmological simulations
Cosmological simulations of galaxy formation are limited by finite computational resources.
Unveil stock correlation via a new tensor-based decomposition method
To retrieve the factor components, we propose a new tensor decomposition (which we name Slice-Diagonal Tensor (SDT) factorization) and compare it to the two most used tensor decompositions, the Tucker and the PARAFAC.
DESCQA: An Automated Validation Framework for Synthetic Sky Catalogs
The use of high-quality simulated sky catalogs is essential for the success of cosmological surveys.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics
The multiplex dependency structure of financial markets
We propose here a multiplex network approach to investigate simultaneously different types of dependency in complex data sets.
Physics and Society Computational Engineering, Finance, and Science Statistical Finance
