Enforcing Analytic Constraints in Neural Networks Emulating Physical Systems

Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations

(2020) Maziar Raissi et al.   SCIENCE

Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions

(2020) Janni Yuval et al.   Nature Communications

Applications of Deep Learning to Ocean Data Inference and Sub-Grid Parameterisation

(2019) Thomas Bolton et al.   Journal of Advances in Modeling Earth Systems

Machine learning for data-driven discovery in solid Earth geoscience

(2019) Karianne J. Bergen et al.   SCIENCE

Deep learning and process understanding for data-driven Earth system science

(2019) Markus Reichstein et al.   NATURE

Learning data-driven discretizations for partial differential equations

(2019) Yohai Bar-Sinai et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data

(2019) Luning Sun et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Could Machine Learning Break the Convection Parameterization Deadlock?

(2018) P. Gentine et al.   GEOPHYSICAL RESEARCH LETTERS

Prognostic Validation of a Neural Network Unified Physics Parameterization

(2018) N. D. Brenowitz et al.   GEOPHYSICAL RESEARCH LETTERS

Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework

(2018) Jin-Long Wu et al.   Physical Review Fluids

Deep learning to represent subgrid processes in climate models

(2018) Stephan Rasp et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data

(2017) Anuj Karpatne et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

Climate goals and computing the future of clouds

(2017) Tapio Schneider et al.   Nature Climate Change

Reynolds averaged turbulence modelling using deep neural networks with embedded invariance

(2016) Julia Ling et al.   JOURNAL OF FLUID MECHANICS

ImageNet Large Scale Visual Recognition Challenge

(2015) Olga Russakovsky et al.   INTERNATIONAL JOURNAL OF COMPUTER VISION

Restricting 32-128 km horizontal scales hardly affects the MJO in the Superparameterized Community Atmosphere Model v.3.0 but the number of cloud-resolving grid columns constrains vertical mixing

(2014) Michael S. Pritchard et al.   Journal of Advances in Modeling Earth Systems

Moist Static Energy Budget of MJO-like Disturbances in the Atmosphere of a Zonally Symmetric Aquaplanet

(2011) Joseph Allan Andersen et al.   JOURNAL OF CLIMATE