Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High-Resolution Simulations

31 August 2017

Papers citing "Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High-Resolution Simulations"

23 / 23 papers shown

Title
When are dynamical systems learned from time series data statistically accurate? Jeongjin Park Nicole Yang Nisha Chandramoorthy AI4TS 36 4 0 09 Nov 2024
Enhancing Low-Order Discontinuous Galerkin Methods with Neural Ordinary Differential Equations for Compressible Navier--Stokes Equations Shinhoo Kang Emil M. Constantinescu AI4CE 22 0 0 29 Oct 2023
Flow Annealed Kalman Inversion for Gradient-Free Inference in Bayesian Inverse Problems R. Grumitt M. Karamanis U. Seljak 43 1 0 20 Sep 2023
Inductive biases in deep learning models for weather prediction Jannik Thümmel Matthias Karlbauer S. Otte C. Zarfl Georg Martius ... Thomas Scholten Ulrich Friedrich V. Wulfmeyer B. Goswami Martin Volker Butz AI4CE 43 6 0 06 Apr 2023
Gradient Flows for Sampling: Mean-Field Models, Gaussian Approximations and Affine Invariance Yifan Chen Daniel Zhengyu Huang Jiaoyang Huang Sebastian Reich Andrew M. Stuart 19 17 0 21 Feb 2023
Data-driven and machine-learning based prediction of wave propagation behavior in dam-break flood Changli Li Zheng Han Yan-ge Li Ming Li Wei-dong Wang 16 2 0 19 Sep 2022
Differentiable Programming for Earth System Modeling Maximilian Gelbrecht Alistair J R White S. Bathiany Niklas Boers 21 16 0 29 Aug 2022
Semi-automatic tuning of coupled climate models with multiple intrinsic timescales: lessons learned from the Lorenz96 model Redouane Lguensat Julie Deshayes Homer Durand Venkatramani Balaji 38 4 0 11 Aug 2022
Explaining the physics of transfer learning a data-driven subgrid-scale closure to a different turbulent flow Adam Subel Yifei Guan A. Chattopadhyay P. Hassanzadeh AI4CE 32 41 0 07 Jun 2022
Productive Performance Engineering for Weather and Climate Modeling with Python Tal Ben-Nun Linus Groner Florian Deconinck Tobias Wicky Eddie Davis ... Lukas Trumper E. Wu O. Fuhrer T. Schulthess Torsten Hoefler 27 16 0 09 May 2022
A posteriori learning for quasi-geostrophic turbulence parametrization Hugo Frezat Julien Le Sommer Ronan Fablet G. Balarac Redouane Lguensat 27 56 0 08 Apr 2022
Artificial intelligence for Sustainable Energy: A Contextual Topic Modeling and Content Analysis T. Saheb Mohammad Dehghani 29 55 0 02 Oct 2021
Combining machine learning and data assimilation to forecast dynamical systems from noisy partial observations Georg Gottwald Sebastian Reich AI4CE 46 37 0 08 Aug 2021
Loosely Conditioned Emulation of Global Climate Models With Generative Adversarial Networks Alexis Ayala Christopher Drazic Brian Hutchinson Ben Kravitz Claudia Tebaldi GAN AI4Cl AI4CE 33 6 0 29 Apr 2021
Using Machine Learning at Scale in HPC Simulations with SmartSim: An Application to Ocean Climate Modeling Sam Partee M. Ellis Alessandro Rigazzi S. Bachman Gustavo M. Marques Andrew Shao Benjamin Robbins AI4Cl AI4CE 22 19 0 13 Apr 2021
Neural Closure Models for Dynamical Systems Abhinav Gupta Pierre FJ Lermusiaux AI4CE 27 45 0 27 Dec 2020
Calibration and Uncertainty Quantification of Convective Parameters in an Idealized GCM Oliver R. A. Dunbar A. Garbuno-Iñigo T. Schneider Andrew M. Stuart 19 58 0 24 Dec 2020
Machine Learning for Robust Identification of Complex Nonlinear Dynamical Systems: Applications to Earth Systems Modeling Nishant Yadav S. Ravela A. Ganguly OOD AI4Cl AI4CE 14 3 0 12 Aug 2020
Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances Sijie He Xinyan Li T. DelSole Pradeep Ravikumar A. Banerjee AI4Cl 29 43 0 14 Jun 2020
Data-driven prediction of a multi-scale Lorenz 96 chaotic system using deep learning methods: Reservoir computing, ANN, and RNN-LSTM A. Chattopadhyay P. Hassanzadeh D. Subramanian AI4CE 11 40 0 20 Jun 2019
Applying machine learning to improve simulations of a chaotic dynamical system using empirical error correction P. Watson AI4Cl AI4CE 27 63 0 24 Apr 2019
Representing ill-known parts of a numerical model using a machine learning approach J. Brajard A. Charantonis J. Sirven 25 3 0 18 Mar 2019
MCMC using Hamiltonian dynamics Radford M. Neal 185 3,267 0 09 Jun 2012