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Numerical simulations in climate, chemistry, or astrophysics are computationally too expensive for uncertainty quantification or parameter-exploration at high-resolution.
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Discovering governing equations from data by sparse identification of nonlinear dynamical systems
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Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
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Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling
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Challenges and design choices for global weather and climate models based on machine learning
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Near-global climate simulation at 1 km resolution: establishing a performance baseline on 4888 gpus with cosmo 5.0
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Could machine learning break the convection parameterization deadlock?
P. Gentine, M. Pritchard, S. Rasp, G. Reinaudi, and G. Yacalis · 2018
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Memory matters: A case for granger causality in climate variability studies
Marie C. McGraw and Elizabeth A. Barnes · 2018
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Paul A. O’Gorman and John G. Dwyer · 2018
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Turbulent superstructures in rayleigh-bénard convection
Ambrish Pandey, Janet D. Scheel, and Jörg Schumacher · 2018
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Model-free prediction of large spatiotemporally chaotic systems from data: A reservoir computing approach
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Justin Sirignano, Jonathan F. MacArt, and Jonathan B. Freund · 2020
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Adversarial super-resolution of climatological wind and solar data
Karen Stengel, Andrew Glaws, Dylan Hettinger, and Ryan N. King · 2020
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Physically interpretable neural networks for the geosciences: Applications to earth system variability
Benjamin A. Toms, Elizabeth A. Barnes, and Imme Ebert-Uphoff · 2020
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Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers
Kiwon Um, Robert Brand, Yun Fei, Philipp Holl, and Nils Thuerey · 2020
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Enforcing statistical constraints in generative adversarial networks for modeling chaotic dynamical systems
Jin-Long Wu, Karthik Kashinath, Adrian Albert, Dragos Chirila, Prabhat, and Heng Xiao · 2020
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Jaideep Pathak, Brian Hunt, Michelle Girvan, Zhixin Lu, and Edward Ott · 2018
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Deep learning to represent subgrid processes in climate models
Stephan Rasp, Michael S. Pritchard, and Pierre Gentine · 2018
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Tensor field networks: Rotation- and translation-equivariant neural networks for 3d point clouds
Nathaniel Thomas, Tess E. Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley · 2018
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Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework
Jin-Long Wu, Heng Xiao, and Eric Paterson · 2018
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Tempogan: A temporally coherent, volumetric gan for super-resolution fluid flow
You Xie, Erik Franz, Mengyu Chu, and Nils Thuerey · 2018
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Deep potential molecular dynamics: A scalable model with the accuracy of quantum mechanics
Linfeng Zhang, Jiequn Han, Han Wang, Roberto Car, and Weinan E · 2018
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Achieving Conservation of Energy in Neural Network Emulators for Climate Modeling
Tom Beucler, Stephan Rasp, Michael Pritchard, and Pierre Gentine · 2019
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Machine learning and the physical sciences
Giuseppe Carleo, Ignacio Cirac, Kyle Cranmer, Laurent Daudet, Maria Schuld, Naftali Tishby, Leslie Vogt-Maranto, and Lenka Zdeborová · 2019
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Systems biology informed deep learning for inferring parameters and hidden dynamics
Alireza Yazdani, Lu Lu, Maziar Raissi, and George Em Karniadakis · 2020
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Graph neural networks: A review of methods and applications
Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun · 2020
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Using physics-informed enhanced super-resolution generative adversarial networks for subfilter modeling in turbulent reactive flows
Mathis Bode, Michael Gauding, Zeyu Lian, Dominik Denker, Marco Davidovic, Konstantin Kleinheinz, Jenia Jitsev, and Heinz Pitsch · 2021
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Geometric deep learning: Grids, groups, graphs, geodesics, and gauges
Michael M. Bronstein, Joan Bruna, Taco Cohen, and Petar Velickovic · 2021
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The role of surrogate models in the development of digital twins of dynamic systems
S. Chakraborty, S. Adhikari, and R. Ganguli · 2021
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DC3: A learning method for optimization with hard constraints
Priya L. Donti, David Rolnick, and J Zico Kolter · 2021
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Multiwavelet-based operator learning for differential equations
Gaurav Gupta, Xiongye Xiao, and Paul Bogdan · 2021
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Liquid time-constant networks
Ramin Hasani, Mathias Lechner, Alexander Amini, Daniela Rus, and Radu Grosu · 2021
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Physics-guided machine learning for scientific discovery: An application in simulating lake temperature profiles
Xiaowei Jia, Jared Willard, Anuj Karpatne, Jordan S. Read, Jacob A. Zwart, Michael Steinbach, and Vipin Kumar · 2021
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Digital Twin Earth – Coasts: Developing a fast and physics-informed surrogate model for coastal floods via neural operators
Peishi Jiang, Nis Meinert, Helga Jordão, Constantin Weisser, Simon Holgate, Alexander Lavin, Björn Lütjens, Dava Newman, Haruko Wainwright, Catherine Walker, and Patrick Barnard · 2021
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Physics-informed machine learning
George Em Karniadakis, Ioannis G. Kevrekidis, Lu Lu, Paris Perdikaris, Sifan Wang, and Liu Yang · 2021
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Physics-informed machine learning: case studies for weather and climate modelling
K. Kashinath, M. Mustafa, A. Albert, J-L. Wu, C. Jiang, S. Esmaeilzadeh, K. Azizzadenesheli, R. Wang, A. Chattopadhyay, A. Singh, A. Manepalli, D. Chirila, R. Yu, R. Walters, B. White, H. Xiao, H. A. Tchelepi, P. Marcus, A. Anandkumar, P. Hassanzadeh, and null Prabhat · 2021
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Wisosuper: Benchmarking super-resolution methods on wind and solar data
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A learning-based multiscale method and its application to inelastic impact problems
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Learning nonlinear operators via deeponet based on the universal approximation theorem of operators
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Spectral PINNs: Fast uncertainty propagation with physics-informed neural networks
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Reservoir computing in reduced order modeling for chaotic dynamical systems
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