Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Karthik Kashinath"'
Autor:
Timothy B. Higgins, Aneesh C. Subramanian, Andre Graubner, Lukas Kapp‐Schwoerer, Peter A. G. Watson, Sarah Sparrow, Karthik Kashinath, Sol Kim, Luca Delle Monache, Will Chapman
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 15, Iss 4, Pp n/a-n/a (2023)
Abstract There is currently large uncertainty over the impacts of climate change on precipitation trends over the US west coast. Atmospheric rivers (ARs) are a significant source of US west coast precipitation and trends in ARs can provide insight in
Externí odkaz:
https://doaj.org/article/4b94c1fd094b4bcea6effe0fa9dcc650
Autor:
Grzegorz Muszynski, Vitaliy Kurlin, Dmitriy Morozov, Michael Wehner, Karthik Kashinath, Prabhat Ram
Publikováno v:
Special Publications. :221-235
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ee0695be92ecc90dc0dfdc606352a11a
https://doi.org/10.1002/9781119467557.ch13
https://doi.org/10.1002/9781119467557.ch13
Autor:
Mayur Mudigonda, Prabhat Ram, Karthik Kashinath, Evan Racah, Ankur Mahesh, Yunjie Liu, Christopher Beckham, Jim Biard, Thorsten Kurth, Sookyung Kim, Samira Kahou, Tegan Maharaj, Burlen Loring, Christopher Pal, Travis O'Brien, Kenneth E. Kunkel, Michael F. Wehner, William D. Collins
Publikováno v:
Deep learning for the Earth Sciences
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ff3a0cf3fe5c937a9fde02021e27740b
https://doi.org/10.1002/9781119646181.ch12
https://doi.org/10.1002/9781119646181.ch12
Autor:
Andrew Lou, Sathyavat Chandran, Mayur Mudigonda, Lukas Kapp-Schwoerer, Ankur Mahesh, Annette Greiner, Prabhat, Katherine Dagon, Thorsten Kurth, Ege Karaismailoglu, W. Chapman, William D. Collins, Andre Graubner, Karthik Kashinath, Ben Toms, Jiayi Chen, Sol Kim, Colby Lewis, Christine A. Shields, Leo von Kleist, Michael Wehner, Travis A. O'Brien, Kevin Yang
Publikováno v:
Geoscientific Model Development, Vol 14, Pp 107-124 (2021)
Identifying, detecting, and localizing extreme weather events is a crucial first step in understanding how they may vary under different climate change scenarios. Pattern recognition tasks such as classification, object detection, and segmentation (i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bfd3be8619132ccee57e29942fad10e3
https://doi.org/10.5194/gmd-14-107-2021
https://doi.org/10.5194/gmd-14-107-2021
Autor:
Allison B. Marquardt Collow, Jonathan J. Rutz, Gary A. Wick, Christine A. Shields, Karthik Kashinath, Anna Wilson, Alexandre M. Ramos, Michael Wehner, Tamara Shulgina, Harinarayan Krishnan, Naomi Goldenson, Scott Sellars, Elizabeth McClenny, Swen Brands, Daniel Walton, Maximiliano Viale, Ashley E. Payne, Prabhat, Vitaliy Kurlin, Irina Gorodetskaya, Grzegorz Muszynski, Travis A. O'Brien, Helen Griffith, David A. Lavers, Duane E. Waliser, Gudrun Magnusdottir, Paul A. Ullrich, Kelly Mahoney, Chandan Sarangi, Ricardo Tomé, Bin Guan, Juan M. Lora, Brian Kawzenuk, Phu Nguyen, Yun Qian, F. Martin Ralph, L. Ruby Leung
Publikováno v:
Journal of Geophysical Research: Atmospheres
Author(s): Rutz, JJ; Shields, CA; Lora, JM; Payne, AE; Guan, B; Ullrich, P; O’Brien, T; Leung, LR; Ralph, FM; Wehner, M; Brands, S; Collow, A; Goldenson, N; Gorodetskaya, I; Griffith, H; Kashinath, K; Kawzenuk, B; Krishnan, H; Kurlin, V; Lavers, D;
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::44f63e339a03c6051276b8f2009c2af7
https://doi.org/10.1029/2019jd030936
https://doi.org/10.1029/2019jd030936
Publikováno v:
Geoscientific Model Development, vol 14, iss 7
Geoscientific Model Development, Vol 14, Pp 4495-4508 (2021)
Geoscientific Model Development, Vol 14, Pp 4495-4508 (2021)
We test the reliability of two neural network interpretation techniques, backward optimization and layerwise relevance propagation, within geoscientific applications by applying them to a commonly studied geophysical phenomenon, the Madden-Julian Osc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9980d9bebfeaefd9999d1971a3c1fc78
https://escholarship.org/uc/item/97j0b5t5
https://escholarship.org/uc/item/97j0b5t5
Publikováno v:
Proceedings of the Combustion Institute. 37:5315-5323
Open-loop forcing is known to be an effective strategy for controlling self-excited thermoacoustic oscillations, but the details of this synchronization process have yet to be comprehensively explored. In this study, we experimentally examine the syn
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7c585464d52f4f3bd26446de7765193f
https://doi.org/10.1016/j.proci.2018.07.077
https://doi.org/10.1016/j.proci.2018.07.077
There is growing interest in data-driven weather prediction (DDWP), for example using convolutional neural networks such as U-NETs that are trained on data from models or reanalysis. Here, we propose 3 components to integrate with commonly used DDWP
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::88866e916595937388a892350766259a
https://doi.org/10.5194/gmd-2021-71
https://doi.org/10.5194/gmd-2021-71
Autor:
Dragos B. Chirila, Soheil Esmaeilzadeh, Adrian Albert, Kamyar Azizzadenesheli, Prabhat, Rose Yu, Karthik Kashinath, Heng Xiao, Hamdi A. Tchelepi, Ashesh Chattopadhyay, Brian White, Rui Wang, A. Singh, Animashree Anandkumar, Chiyu \\'Max\\' Jiang, Jin-Long Wu, Ashray Manepalli, Philip Marcus, Pedram Hassanzadeh, Robin Walters, Mustafa Mustafa
Publikováno v:
Philosophical transactions. Series A, Mathematical, physical, and engineering sciences. 379(2194)
Machine learning (ML) provides novel and powerful ways of accurately and efficiently recognizing complex patterns, emulating nonlinear dynamics, and predicting the spatio-temporal evolution of weather and climate processes. Off-the-shelf ML models, h
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aaffeb23fee6c2bfbffaa3e96ff3502c
https://pubmed.ncbi.nlm.nih.gov/33583262
https://pubmed.ncbi.nlm.nih.gov/33583262
Autor:
Karthik Kashinath, Mustafa Mustafa, Philip Marcus, Soheil Esmaeilzadeh, Chiyu \\'Max\\' Jiang, Animashree Anandkumar, Kamyar Azizzadenesheli, Prabhat, Hamdi A. Tchelepi
Publikováno v:
SC
We propose MeshfreeFlowNet, a novel deep learning-based super-resolution framework to generate continuous (grid-free) spatio-temporal solutions from the low-resolution inputs. While being computationally efficient, MeshfreeFlowNet accurately recovers
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b087f940e155ec0fbf2e4547037fc26f
https://resolver.caltech.edu/CaltechAUTHORS:20200526-153937049
https://resolver.caltech.edu/CaltechAUTHORS:20200526-153937049