1 code implementation • 23 Jun 2021 • Peter Harrington, Mustafa Mustafa, Max Dornfest, Benjamin Horowitz, Zarija Lukić
Full-physics cosmological simulations are powerful tools for studying the formation and evolution of structure in the universe but require extreme computational resources.
1 code implementation • 16 Mar 2021 • Ashesh Chattopadhyay, Mustafa Mustafa, Pedram Hassanzadeh, Eviatar Bach, Karthik Kashinath
These components are 1) a deep spatial transformer added to the latent space of the U-NETs to preserve a property called equivariance, which is related to correctly capturing rotations and scalings of features in spatio-temporal data, 2) a data-assimilation (DA) algorithm to ingest noisy observations and improve the initial conditions for next forecasts, and 3) a multi-time-step algorithm, which combines forecasts from DDWP models with different time steps through DA, improving the accuracy of forecasts at short intervals.
no code implementations • 12 Jan 2021 • Md Abul Hayat, Peter Harrington, George Stein, Zarija Lukić, Mustafa Mustafa
We use a contrastive self-supervised learning framework to estimate distances to galaxies from their photometric images.
1 code implementation • 24 Dec 2020 • Md Abul Hayat, George Stein, Peter Harrington, Zarija Lukić, Mustafa Mustafa
We show that, without the need for labels, self-supervised learning recovers representations of sky survey images that are semantically useful for a variety of scientific tasks.
no code implementations • 30 Sep 2020 • Jaideep Pathak, Mustafa Mustafa, Karthik Kashinath, Emmanuel Motheau, Thorsten Kurth, Marcus Day
As a proof-of-concept, we demonstrate our ML-PDE strategy on a two-dimensional turbulent (Rayleigh Number $Ra=10^9$) Rayleigh-B\'enard Convection (RBC) problem.
3 code implementations • 1 Jun 2020 • Zhewei Yao, Amir Gholami, Sheng Shen, Mustafa Mustafa, Kurt Keutzer, Michael W. Mahoney
We introduce ADAHESSIAN, a second order stochastic optimization algorithm which dynamically incorporates the curvature of the loss function via ADAptive estimates of the HESSIAN.
1 code implementation • 1 May 2020 • Chiyu Max Jiang, Soheil Esmaeilzadeh, Kamyar Azizzadenesheli, Karthik Kashinath, Mustafa Mustafa, Hamdi A. Tchelepi, Philip Marcus, Prabhat, Anima Anandkumar
We propose MeshfreeFlowNet, a novel deep learning-based super-resolution framework to generate continuous (grid-free) spatio-temporal solutions from the low-resolution inputs.
1 code implementation • 20 Nov 2019 • Rui Wang, Karthik Kashinath, Mustafa Mustafa, Adrian Albert, Rose Yu
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, training, and inference of such models.
2 code implementations • 7 Jun 2017 • Mustafa Mustafa, Deborah Bard, Wahid Bhimji, Zarija Lukić, Rami Al-Rfou, Jan M. Kratochvil
Inferring model parameters from experimental data is a grand challenge in many sciences, including cosmology.