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S. Karthik Mukkavilli
S. Karthik Mukkavilli
Mercuria
Подтвержден адрес электронной почты в домене mercuria.com - Главная страница
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Процитировано
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Tackling climate change with machine learning
D Rolnick, PL Donti, LH Kaack, K Kochanski, A Lacoste, K Sankaran, ...
ACM Computing Surveys (CSUR) 55 (2), 1-96, 2022
1337*2022
Drawdown: The most comprehensive plan ever proposed to reverse global warming
P Hawken, C Frischmann, K Wilkinson, R Allard, K Bayuk, JP Gouveia, ...
Penguin, 2017
10572017
Foundation models for generalist geospatial artificial intelligence
J Jakubik, S Roy, CE Phillips, P Fraccaro, D Godwin, B Zadrozny, ...
CoRR, 2023
602023
Assessment of atmospheric aerosols from two reanalysis products over Australia
SK Mukkavilli, AA Prasad, RA Taylor, J Huang, RM Mitchell, A Troccoli, ...
Atmospheric research 215, 149-164, 2019
562019
Visualizing the consequences of climate change using cycle-consistent adversarial networks
V Schmidt, A Luccioni, SK Mukkavilli, N Balasooriya, K Sankaran, ...
International Conference on Learning Representations (ICLR), AI for Social …, 2019
542019
AI Foundation Models for Weather and Climate: Applications, Design, and Implementation
S Karthik Mukkavilli, D Salles Civitarese, J Schmude, J Jakubik, A Jones, ...
arXiv preprint arXiv: 2309.10808, 2023
22*2023
Mesoscale simulations of Australian direct normal irradiance, featuring an extreme dust event
SK Mukkavilli, AA Prasad, RA Taylor, A Troccoli, MJ Kay
Journal of Applied Meteorology and Climatology 57 (3), 493-515, 2018
202018
Strategic foresight to applications of artificial intelligence to achieve water-related sustainable development goals
H Mehmood, SK Mukkavilli, I Weber, A Koshio, C Meechaiya, T Piman, ...
United Nations University Institute for Water, Environment an d Health …, 2020
142020
Indus: Effective and efficient language models for scientific applications
B Bhattacharjee, A Trivedi, M Muraoka, M Ramasubramanian, ...
arXiv preprint arXiv:2405.10725, 2024
52024
Lifelines for a Drowning Science‐Improving Findability and Synthesis of Hydrologic Publications
L Stein, SK Mukkavilli, T Wagener
Hydrological Processes, e14742, 2022
52022
EnviroNet: ImageNet for Environment
SK Mukkavilli, P Tissot, A Ganguly, L Joppa, D Meger, G Dudek
18th Conference on Artificial and Computational Intelligence and its …, 2019
5*2019
Predicting ice flow using machine learning
Y Min, SK Mukkavilli, Y Bengio
Neural Information Processing Systems (NeurIPS), Tackling Climate Change …, 2019
4*2019
Deep learning for Aerosol Forecasting
C Hoyne, SK Mukkavilli, D Meger
Neural Information Processing Systems (NeurIPS), Machine Learning and the …, 2019
42019
TensorBank: Tensor Lakehouse for Foundation Model Training
R Kienzler, J Schmude, N Simumba, B Blumenstiel, M Freitag, D Kimura, ...
2023 IEEE International Conference on Big Data (BigData), 3350-3354, 2023
22023
Wealth over Woe: Global biases in hydro‐hazard research
L Stein, SK Mukkavilli, BM Pfitzmann, PWJ Staar, U Ozturk, C Berrospi, ...
Earth's Future 12 (10), e2024EF004590, 2024
12024
AI-driven autonomous microrobots for targeted medicine
M Medany, SK Mukkavilli, D Ahmed
Nature Reviews Bioengineering, 2024
12024
Generative large eddy simulations with conditional variational autoencoders
SK Mukkavilli, MS Pritchard, KG Pressel, G Mooers, PL Ma, S Mandt
AGU Fall Meeting Abstracts 2020, A043-0009, 2020
12020
Investigating Australian dust aerosol spatiotemporal effects on direct normal irradiance forecasts
SK Mukkavilli
https://www.unsworks.unsw.edu.au/primo-explore/fulldisplay/unsworks_58614 …, 2018
12018
Site-scale methane plume simulation and validation from oil and gas facilities through advanced dispersion, atmospheric modeling, and scientific machine learning
A Fathi, JLS Almeida, E Bentivegna, F Cardoso, B Elmegreen, L Klein, ...
AGU24, 2024
2024
Efficient Fine-tuning of Atmospheric Foundation Models for Wildfire Modeling
AA Rahman, L Mackey, SK Mukkavilli, S Kahou
AGU24, 2024
2024
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Статьи 1–20