113 citations · 153 across the 6 of their papers we have counts for
7 papers
Estimating Galactic Distances From Images Using Self-supervised Representation Learning
Md Abul Hayat, Peter Harrington, George Stein +2
We use a contrastive self-supervised learning framework to estimate distances to galaxies from their photometric images. We incorporate data augmentations from computer vision as w…
Using Machine Learning to Augment Coarse-Grid Computational Fluid Dynamics Simulations
Jaideep Pathak, Mustafa Mustafa, Karthik Kashinath +3
Simulation of turbulent flows at high Reynolds number is a computationally challenging task relevant to a large number of engineering and scientific applications in diverse fields…
ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning
Zhewei Yao, Amir Gholami, Sheng Shen +3
We introduce ADAHESSIAN, a second order stochastic optimization algorithm which dynamically incorporates the curvature of the loss function via ADAptive estimates of the HESSIAN. S…
Towards Physics-informed Deep Learning for Turbulent Flow Prediction
Rui Wang, Karthik Kashinath, Mustafa Mustafa +2
While deep learning has shown tremendous success in a wide range of domains, it remains a grand challenge to incorporate physical principles in a systematic manner to the design, t…
Scaling GRPC Tensorflow on 512 nodes of Cori Supercomputer
Amrita Mathuriya, Thorsten Kurth, Vivek Rane +5
We explore scaling of the standard distributed Tensorflow with GRPC primitives on up to 512 Intel Xeon Phi (KNL) nodes of Cori supercomputer with synchronous stochastic gradient de…
CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks
Mustafa Mustafa, Deborah Bard, Wahid Bhimji +3
Inferring model parameters from experimental data is a grand challenge in many sciences, including cosmology. This often relies critically on high fidelity numerical simulations, w…