361 citations · 1.2k across the 40 of their papers we have counts for
8 papers · 1 filter
Probabilistic neural network-based reduced-order surrogate for fluid flows
Kai Fukami, Romit Maulik, Nesar Ramachandra +2
In recent years, there have been a surge in applications of neural networks (NNs) in physical sciences. Although various algorithmic advances have been proposed, there are, thus fa…
Anomaly Detection in Astronomical Images with Generative Adversarial Networks
Kate Storey-Fisher, Marc Huertas-Company, Nesar Ramachandra +4
We present an anomaly detection method using Wasserstein generative adversarial networks (WGANs) on optical galaxy images from the wide-field survey conducted with the Hyper Suprim…
Peculiar Velocity Estimation from Kinetic SZ Effect using Deep Neural Networks
Yuyu Wang, Nesar Ramachandra, Edgar M. Salazar-Canizales +3
The Sunyaev-Zel'dolvich (SZ) effect is expected to be instrumental in measuring velocities of distant clusters in near future telescope surveys. We simplify the calculation of pecu…
Matter Power Spectrum Emulator for f(R) Modified Gravity Cosmologies
Nesar Ramachandra, Georgios Valogiannis, Mustapha Ishak +1
Testing a subset of viable cosmological models beyond General Relativity (GR), with implications for cosmic acceleration and the Dark Energy associated with it, is within the reach…
Beyond the Hubble Sequence -- Exploring Galaxy Morphology with Unsupervised Machine Learning
Ting-Yun Cheng, Marc Huertas-Company, Christopher J. Conselice +3
We explore unsupervised machine learning for galaxy morphology analyses using a combination of feature extraction with a vector-quantised variational autoencoder (VQ-VAE) and hiera…
Latent-space time evolution of non-intrusive reduced-order models using Gaussian process emulation
Romit Maulik, Themistoklis Botsas, Nesar Ramachandra +2
Non-intrusive reduced-order models (ROMs) have recently generated considerable interest for constructing computationally efficient counterparts of nonlinear dynamical systems emerg…
Probabilistic neural networks for fluid flow surrogate modeling and data recovery
Romit Maulik, Kai Fukami, Nesar Ramachandra +2
We consider the use of probabilistic neural networks for fluid flow {surrogate modeling} and data recovery. This framework is constructed by assuming that the target variables are…
From the Inner to Outer Milky Way: A Photometric Sample of 2.6 Million Red Clump Stars
Madeline Lucey, Yuan-Sen Ting, Nesar S. Ramachandra +1
Large pristine samples of red clump stars are highly sought after given that they are standard candles and give precise distances even at large distances. However, it is difficult…