Zobrazeno 1 - 10
of 51
pro vyhledávání: '"Yu, Sungduk"'
Autor:
Hinck, Musashi, Holtermann, Carolin, Olson, Matthew Lyle, Schneider, Florian, Yu, Sungduk, Bhiwandiwalla, Anahita, Lauscher, Anne, Tseng, Shaoyen, Lal, Vasudev
We uncover a surprising multilingual bias occurring in a popular class of multimodal vision-language models (VLMs). Including an image in the query to a LLaVA-style VLM significantly increases the likelihood of the model returning an English response
Externí odkaz:
http://arxiv.org/abs/2407.02333
Autor:
Hu, Zeyuan, Subramaniam, Akshay, Kuang, Zhiming, Lin, Jerry, Yu, Sungduk, Hannah, Walter M., Brenowitz, Noah D., Romero, Josh, Pritchard, Michael S.
Modern climate projections often suffer from inadequate spatial and temporal resolution due to computational limitations, resulting in inaccurate representations of sub-grid processes. A promising technique to address this is the Multiscale Modeling
Externí odkaz:
http://arxiv.org/abs/2407.00124
Autor:
Behrens, Gunnar, Beucler, Tom, Iglesias-Suarez, Fernando, Yu, Sungduk, Gentine, Pierre, Pritchard, Michael, Schwabe, Mierk, Eyring, Veronika
Deep learning has proven to be a valuable tool to represent subgrid processes in climate models, but most application cases have so far used idealized settings and deterministic approaches. Here, we develop ensemble and stochastic parameterizations w
Externí odkaz:
http://arxiv.org/abs/2402.03079
Autor:
Nathaniel, Juan, Qu, Yongquan, Nguyen, Tung, Yu, Sungduk, Busecke, Julius, Grover, Aditya, Gentine, Pierre
Accurate prediction of climate in the subseasonal-to-seasonal scale is crucial for disaster preparedness and robust decision making amidst climate change. Yet, forecasting beyond the weather timescale is challenging because it deals with problems oth
Externí odkaz:
http://arxiv.org/abs/2402.00712
Accurate and computationally-viable representations of clouds and turbulence are a long-standing challenge for climate model development. Traditional parameterizations that crudely but efficiently approximate these processes are a leading source of u
Externí odkaz:
http://arxiv.org/abs/2401.02098
Autor:
Lin, Jerry, Yu, Sungduk, Peng, Liran, Beucler, Tom, Wong-Toi, Eliot, Hu, Zeyuan, Gentine, Pierre, Geleta, Margarita, Pritchard, Mike
Machine-learning (ML) parameterizations of subgrid processes (here of turbulence, convection, and radiation) may one day replace conventional parameterizations by emulating high-resolution physics without the cost of explicit simulation. However, the
Externí odkaz:
http://arxiv.org/abs/2309.16177
Autor:
Yu, Sungduk, Hu, Zeyuan, Subramaniam, Akshay, Hannah, Walter, Peng, Liran, Lin, Jerry, Bhouri, Mohamed Aziz, Gupta, Ritwik, Lütjens, Björn, Will, Justus C., Behrens, Gunnar, Busecke, Julius J. M., Loose, Nora, Stern, Charles I., Beucler, Tom, Harrop, Bryce, Heuer, Helge, Hillman, Benjamin R., Jenney, Andrea, Liu, Nana, White, Alistair, Zheng, Tian, Kuang, Zhiming, Ahmed, Fiaz, Barnes, Elizabeth, Brenowitz, Noah D., Bretherton, Christopher, Eyring, Veronika, Ferretti, Savannah, Lutsko, Nicholas, Gentine, Pierre, Mandt, Stephan, Neelin, J. David, Yu, Rose, Zanna, Laure, Urban, Nathan, Yuval, Janni, Abernathey, Ryan, Baldi, Pierre, Chuang, Wayne, Huang, Yu, Iglesias-Suarez, Fernando, Jantre, Sanket, Ma, Po-Lun, Shamekh, Sara, Zhang, Guang, Pritchard, Michael
Modern climate projections lack adequate spatial and temporal resolution due to computational constraints, leading to inaccuracies in representing critical processes like thunderstorms that occur on the sub-resolution scale. Hybrid methods combining
Externí odkaz:
http://arxiv.org/abs/2306.08754
Publikováno v:
Artificial Intelligence for the Earth Systems, 3(1), 2024, e230013
Hyperparameter optimization (HPO) is an important step in machine learning (ML) model development, but common practices are archaic -- primarily relying on manual or grid searches. This is partly because adopting advanced HPO algorithms introduces ad
Externí odkaz:
http://arxiv.org/abs/2302.03845
Autor:
Beucler, Tom, Gentine, Pierre, Yuval, Janni, Gupta, Ankitesh, Peng, Liran, Lin, Jerry, Yu, Sungduk, Rasp, Stephan, Ahmed, Fiaz, O'Gorman, Paul A., Neelin, J. David, Lutsko, Nicholas J., Pritchard, Michael
Projecting climate change is a generalization problem: we extrapolate the recent past using physical models across past, present, and future climates. Current climate models require representations of processes that occur at scales smaller than model
Externí odkaz:
http://arxiv.org/abs/2112.08440
Autor:
Mamalakis, Antonios, Randerson, James T., Yu, Jin-Yi, Pritchard, Michael S., Magnusdottir, Gudrun, Smyth, Padhraic, Levine, Paul A., Yu, Sungduk, Foufoula-Georgiou, Efi
Publikováno v:
Nature Climate Change 2021
Future changes in the location of the intertropical convergence zone (ITCZ) due to climate change are of high interest since they could substantially alter precipitation patterns in the tropics and subtropics. Although models predict a future narrowi
Externí odkaz:
http://arxiv.org/abs/2007.00239