From aebacde84c06ff03b28355b0ebe44213c8dbf8fb Mon Sep 17 00:00:00 2001 From: Shubham Gupta Date: Mon, 26 Aug 2024 10:00:37 -0400 Subject: [PATCH] adjust logo size, new research --- content/publications/_index.md | 90 +++++++++++++---------- static/images/publications/dsga_2024.png | Bin 0 -> 1502404 bytes 2 files changed, 52 insertions(+), 38 deletions(-) create mode 100644 static/images/publications/dsga_2024.png diff --git a/content/publications/_index.md b/content/publications/_index.md index f0fd07b4..aa88aa6e 100644 --- a/content/publications/_index.md +++ b/content/publications/_index.md @@ -5,12 +5,26 @@ heroSubHeading: 'Research work and relevant papers by our team' heroBackground: 'images/susan-q-yin-2JIvboGLeho-unsplash.jpg' --- -## M²LInES research publications +## M²LInES research and other relevant publications If you are interested in understanding how M²LInES is using machine learning to improve climate models, we have developed an educational JupyterBook [Learning Machine Learning for Climate modeling with Lorenz 96](https://m2lines.github.io/L96_demo). This JupyterBook describes the key research themes in M²LInES, through the use of a simple climate model and machine learning algorithms. You can run the notebooks yourself, contribute to the development of the JupyterBook or let us know what you think on GitHub https://github.com/m2lines/L96_demo. +DOI icon M²LInES funded research + ### 2024 +
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+

+ DOI icon + Surya Dheeshjith, Adam Subel, Shubham Gupta, Alistair Adcroft, Carlos Fernandez-Granda, Julius Busecke, Laure Zanna
+ Transfer Learning for Emulating Ocean Climate Variability across CO2 forcing
+ Preprint accepted at ICML 2024 ML4ESM DOI: 10.48550/arXiv.2405.18585 +

+
+
@@ -41,7 +55,7 @@ If you are interested in understanding how M²LInES is using machine learning to

- DOI icon + DOI icon Lorenzo Zampieri, David Clemens-Sewall, Anne Sledd, Nils Hutter, Marika Holland
Modeling the Winter Heat Conduction Through the Sea Ice System During MOSAiC
Geophysical Research Letters 2024 DOI: 10.1029/2023GL106760 @@ -53,7 +67,7 @@ If you are interested in understanding how M²LInES is using machine learning to

- DOI icon + DOI icon William Gregory, Ronald MacEachern, So Takao, Isobel Lawrence, Carmen Nab, Marc Deisenroth, Michel Tsamados
Scalable interpolation of satellite altimetry data with probabilistic machine learning
Nature Comms. 2024 DOI: 10.21203/rs.3.rs-4209064/v1 @@ -65,7 +79,7 @@ If you are interested in understanding how M²LInES is using machine learning to

- DOI icon + DOI icon Adam Subel, Laure Zanna
Building Ocean Climate Emulators
ICLR 2024 Workshop: Tackling Climate Change with Machine Learning. DOI: 10.48550/arXiv.2402.04342 @@ -78,7 +92,7 @@ If you are interested in understanding how M²LInES is using machine learning to

- DOI icon + DOI icon William Gregory, Mitchell Bushuk, Yongfei Zhang, Alistair Adcroft, Laure Zanna
Machine Learning for Online Sea Ice Bias Correction Within Global Ice-Ocean Simulations
Geophysical Research Letters 2024. DOI: 10.1029/2023GL106776 @@ -90,7 +104,7 @@ If you are interested in understanding how M²LInES is using machine learning to

- DOI icon + DOI icon Fabrizio Falasca, Pavel Perezhogin, Laure Zanna
Data-driven framework for dimensionality reduction and causal inference in climate fields
APS Physics Review E 2024 DOI: 10.1103/PhysRevE.109.044202 @@ -102,7 +116,7 @@ If you are interested in understanding how M²LInES is using machine learning to

- DOI icon + DOI icon Tom Beucler, Michael Pritchard, Janni Yuval, Ankitesh Gupta, Liran Peng, Stephan Rasp, Fiaz Ahmed, Paul O’Gorman, J. David Neelin, Nicholas J. Lutsko, Pierre Gentine
Climate-Invariant Machine Learning
Science Advances 2024 DOI: 10.1126/sciadv.adj7250 @@ -116,7 +130,7 @@ If you are interested in understanding how M²LInES is using machine learning to

- DOI icon + DOI icon Cheng Zhang, Pavel Perezhogin, Cem Gultekin, Alistair Adcroft, Carlos Fernandez-Granda, Laure Zanna
Implementation and Evaluation of a Machine Learned Mesoscale Eddy Parameterization Into a Numerical Ocean Circulation Model
James 2023. DOI: 10.1029/2023MS003697 @@ -128,7 +142,7 @@ If you are interested in understanding how M²LInES is using machine learning to

- DOI icon + DOI icon Pavel Perezhogin, Cheng Zhang, Alistair Adcroft, Carlos Fernandez-Granda, Laure Zanna
Implementation of a data-driven equation-discovery mesoscale parameterization into an ocean model
James 2023. DOI: 10.48550/arXiv.2311.02517 @@ -140,7 +154,7 @@ If you are interested in understanding how M²LInES is using machine learning to

- DOI icon + DOI icon Will Chapman and Judith Berner
Deterministic and stochastic tendency adjustments derivedfrom data assimilation and nudging
QJRMS 2023. DOI: 10.1002/qj.4652 @@ -152,7 +166,7 @@ If you are interested in understanding how M²LInES is using machine learning to

- DOI icon + DOI icon Christian Pedersen, Laure Zanna, Joan Bruna, Pavel Perezhogin
Reliable coarse-grained turbulent simulations through combined offline learning and neural emulation
ICML 2023 Workshop on Synergy of Scientific and Machine Learning Modeling DOI: 10.48550/arXiv.2307.13144 @@ -164,7 +178,7 @@ If you are interested in understanding how M²LInES is using machine learning to

- DOI icon + DOI icon Emily Newsom, Laure Zanna, Jonathan Gregory
Background Pycnocline depth constrains Future Ocean Heat Uptake Efficiency
AGU Geophysical Research Letters 2023. DOI: 10.1029/2023GL105673 @@ -176,7 +190,7 @@ If you are interested in understanding how M²LInES is using machine learning to

- DOI icon + DOI icon Sara Shamekh and Pierre Gentine
Learning Atmospheric Boundary Layer Turbulence
JAMES 2023. DOI: 10.22541/essoar.168748456.60017486/v1 @@ -188,7 +202,7 @@ If you are interested in understanding how M²LInES is using machine learning to

- DOI icon + DOI icon Aakash Sane, Brandon G. Reichl, Alistair Adcroft, Laure Zanna
Parameterizing vertical mixing coefficients in the Ocean Surface Boundary Layer using Neural Networks
@@ -201,7 +215,7 @@ If you are interested in understanding how M²LInES is using machine learning to

- DOI icon + DOI icon Sungduk Yu, ..., Michael S. Pritchard
ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid
multi-scale climate simulators

@@ -214,7 +228,7 @@ If you are interested in understanding how M²LInES is using machine learning to

- DOI icon + DOI icon Karan Jakhar, Yifei Guan, Rambod Mojgani, Ashesh Chattopadhyay, Pedram Hassanzadeh, Laure Zanna
Learning Closed-form Equations for Subgrid-scale Closures from High-fidelity Data: Promises and Challenges.
ESS Open Archive. 2023. DOI: 10.22541/essoar.168677212.21341231/v1 @@ -226,7 +240,7 @@ If you are interested in understanding how M²LInES is using machine learning to

- DOI icon + DOI icon Gustau Camps-Valls, Andreas Gerhardus, Urmi Ninad, Gherardo Varando, Georg Martius,
Emili Balaguer-Ballester, Ricardo Vinuesa, Emiliano Diaz, Laure Zanna, Jakob Runge

Discovering Causal Relations and Equations from Data.
@@ -239,7 +253,7 @@ If you are interested in understanding how M²LInES is using machine learning to

- DOI icon + DOI icon Rei Chemke and Janni Yuval
Human-induced weakening of the Northern Hemisphere tropical circulation
Nature. 2023. DOI: 10.1038/s41586-023-05903-1 @@ -251,7 +265,7 @@ If you are interested in understanding how M²LInES is using machine learning to

- DOI icon + DOI icon William Gregory, Mitchell Bushuk, Alistair Adcroft, Yongfei Zhang, Laure Zanna
Deep learning of systematic sea ice model errors from data assimilation increments
JAMES 2023. DOI: 10.1029/2023MS003757 @@ -264,7 +278,7 @@ If you are interested in understanding how M²LInES is using machine learning to

- DOI icon + DOI icon Janni Yuval and Paul A. O’Gorman
Neural-Network Parameterization of Subgrid Momentum Transport in the Atmosphere
JAMES 2023. DOI: 10.1029/2023MS003606 @@ -276,7 +290,7 @@ If you are interested in understanding how M²LInES is using machine learning to

- DOI icon + DOI icon Karl Otness, Laure Zanna, Joan Bruna
Data-driven multiscale modeling of subgrid parameterizations in climate models
Preprint accepted at ICLR Workshop on Climate Change AI. 2023. DOI: 10.48550/arXiv.2303.17496 @@ -288,7 +302,7 @@ If you are interested in understanding how M²LInES is using machine learning to

- DOI icon + DOI icon Fabrizio Falasca, Andrew Brettin, Laure Zanna, Stephen M. Griffies, Jianjun Yin, Ming Zhao
Exploring the non-stationarity of coastal sea level probability distributions
EDS. volume 2 2023. DOI: 10.1017/eds.2023.10 @@ -300,7 +314,7 @@ If you are interested in understanding how M²LInES is using machine learning to

- DOI icon + DOI icon Pavel Perezhogin, Laure Zanna, Carlos Fernandez-Granda
Generative data-driven approaches for stochastic subgrid parameterizations in an idealized ocean model
JAMES. 2023. DOI: 10.1029/2023MS003681 @@ -312,7 +326,7 @@ If you are interested in understanding how M²LInES is using machine learning to

- DOI icon + DOI icon Pavel Perezhogin, Andrey Glazunov
Subgrid Parameterizations of Ocean Mesoscale Eddies Based on Germano Decomposition
JAMES. 2023. DOI: 10.1029/2023MS003771 @@ -325,7 +339,7 @@ If you are interested in understanding how M²LInES is using machine learning to

- DOI icon + DOI icon Cheng Zhang, Pavel Perezhogin, Cem Gultekin, Alistair Adcroft, Carlos Fernandez-Granda, Laure Zanna
Implementation and Evaluation of a Machine Learned Mesoscale Eddy Parameterization into a
Numerical Ocean Circulation Model

JAMES 2023. DOI: 10.1029/2023MS003697 @@ -337,7 +351,7 @@ If you are interested in understanding how M²LInES is using machine learning to

- DOI icon + DOI icon Qiyu Xiao, Dhruv Balwada, C. Spencer Jones, Mario Herrero-Gonzalez, K. Shafer Smith, Ryan Abernathey
Reconstruction of Surface Kinematics from Sea Surface Height Using Neural Networks
JAMES. 2023. DOI: 10.1029/2022MS003258 @@ -349,7 +363,7 @@ If you are interested in understanding how M²LInES is using machine learning to

- DOI icon + DOI icon Takaya Uchida, Dhruv Balwada, Quentin Jamet, William K. Dewar, Bruno Deremble,
Thierry Penduff, Julien Le Sommer

Cautionary tales from the mesoscale eddy transport tensor
@@ -362,7 +376,7 @@ If you are interested in understanding how M²LInES is using machine learning to

- DOI icon + DOI icon Adam Subel, Yifei Guan, Ashesh Chattopadhyay, Pedram Hassanzadeh
Explaining the physics of transfer learning in data-driven turbulence modeling
PNAS NEXUS 2023. DOI: 10.1093/pnasnexus/pgad015 @@ -374,7 +388,7 @@ If you are interested in understanding how M²LInES is using machine learning to

- DOI icon + DOI icon Lorenzo Zampieri, Gabriele Arduini, Marika Holland, Sarah Keeley, Kristian S. Mogensen,
Matthew D. Shupe, Steffen Tietsche

A Machine Learning Correction Model of the Winter Clear-Sky Temperature Bias over the Arctic Sea Ice in Atmospheric Reanalyses
@@ -387,7 +401,7 @@ If you are interested in understanding how M²LInES is using machine learning to

- DOI icon + DOI icon Lei Chen and Joan Bruna
On Gradient Descent Convergence beyond the Edge of Stability
ICLR 2023 DOI: 10.5555/3618408.3618580 @@ -403,7 +417,7 @@ If you are interested in understanding how M²LInES is using machine learning to

- DOI icon + DOI icon Andrew Ross, Ziwei Li, Pavel Perezhogin, Carlos Fernandez-Granda, Laure Zanna
Benchmarking of machine learning ocean subgrid parameterizations in an idealized model
JAMES. 2022. DOI: 10.1029/2022MS003258 @@ -415,7 +429,7 @@ If you are interested in understanding how M²LInES is using machine learning to

- DOI icon + DOI icon Joan Bruna, Benjamin Peherstorfer, Eric Vanden-Eijnden
Neural Galerkin Scheme with Active Learning for High-Dimensional Evolution Equations
Journal of Computational Physics DOI: 10.1016/j.jcp.2023.112588 @@ -427,7 +441,7 @@ If you are interested in understanding how M²LInES is using machine learning to

- DOI icon + DOI icon Peidong Wang, Janni Yuval, Paul A. O'Gorman
Non-local parameterization of atmospheric subgrid processes with neural networks
JAMES 2022. DOI: 10.1029/2022MS002984 @@ -439,7 +453,7 @@ If you are interested in understanding how M²LInES is using machine learning to

- DOI icon + DOI icon Sara Shamekh, Kara D Lamb, Yu Huang, Pierre Gentine
Implicit learning of convective organization explains precipitation stochasticity
In review. 2022. DOI: 10.1002/essoar.10512517.1 @@ -451,7 +465,7 @@ If you are interested in understanding how M²LInES is using machine learning to

- DOI icon + DOI icon Hannah Christensen and Laure Zanna
Parametrization in Weather and Climate Models
Oxford Research Encyclopedia of Climate Science. 2022. DOI: 10.1093/acrefore/9780190228620.013.826 @@ -463,7 +477,7 @@ If you are interested in understanding how M²LInES is using machine learning to

- DOI icon + DOI icon Sheng Liu, Aakash Kaku, Haoxiang Huang, Laure Zanna, Weicheng Zhu, Narges Razavian,
Matan Leibovich, Sreyas Mohan, Boyang Yu, Jonathan Niles-Weed, Carlos Fernandez-Granda

Deep Probability Estimation
@@ -476,7 +490,7 @@ If you are interested in understanding how M²LInES is using machine learning to

- DOI icon + DOI icon Nora Loose, Ryan Abernathey, Ian Grooms, Julius Busecke, Arthur Guillaumin,
Elizabeth Yankovsky, Gustavo Marques, Jacob Steinberg, Andrew Slavin Ross, Hemant Khatri,
Scott Bachman, Laure Zanna, Paige Martin

@@ -490,7 +504,7 @@ If you are interested in understanding how M²LInES is using machine learning to

- DOI icon + DOI icon Hugo Frezat, Julien Le Sommer, Ronan Fablet, Guillaume Balarac, Redouane Lguensat
A posteriori learning for quasi-geostrophic turbulence parametrization
JAMES. 2022. 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