Zobrazeno 1 - 10
of 2 726
pro vyhledávání: '"P. Parris"'
Many important phenomena in scientific fields such as climate, neuroscience, and epidemiology are naturally represented as spatiotemporal gridded data with complex interactions. For example, in climate science, researchers aim to uncover how large-sc
Externí odkaz:
http://arxiv.org/abs/2411.05331
Autor:
Lyu, Bohan, Cao, Yadi, Watson-Parris, Duncan, Bergen, Leon, Berg-Kirkpatrick, Taylor, Yu, Rose
Large Language Models (LLMs) demonstrate promising capabilities in solving simple scientific problems but often produce hallucinations for complex ones. While integrating LLMs with tools can increase reliability, this approach typically results in ov
Externí odkaz:
http://arxiv.org/abs/2411.00412
Autor:
Manivannan, Veeramakali Vignesh, Jafari, Yasaman, Eranky, Srikar, Ho, Spencer, Yu, Rose, Watson-Parris, Duncan, Ma, Yian, Bergen, Leon, Berg-Kirkpatrick, Taylor
The use of foundation models in climate science has recently gained significant attention. However, a critical issue remains: the lack of a comprehensive evaluation framework capable of assessing the quality and scientific validity of model outputs.
Externí odkaz:
http://arxiv.org/abs/2410.16701
Autor:
Baño-Medina, Jorge, Sengupta, Agniv, Michaelis, Allison, Monache, Luca Delle, Kalansky, Julie, Watson-Parris, Duncan
AI data-driven models (Graphcast, Pangu Weather, Fourcastnet, and SFNO) are explored for storyline-based climate attribution due to their short inference times, which can accelerate the number of events studied, and provide real time attributions whe
Externí odkaz:
http://arxiv.org/abs/2409.11605
Full-complexity Earth system models (ESMs) are computationally very expensive, limiting their use in exploring the climate outcomes of multiple emission pathways. More efficient emulators that approximate ESMs can directly map emissions onto climate
Externí odkaz:
http://arxiv.org/abs/2408.05288
Multi-fidelity surrogate modeling aims to learn an accurate surrogate at the highest fidelity level by combining data from multiple sources. Traditional methods relying on Gaussian processes can hardly scale to high-dimensional data. Deep learning ap
Externí odkaz:
http://arxiv.org/abs/2402.18846
Autor:
Chaudhry, Muhammad Ahmed, Kim, Lyna, Irvin, Jeremy, Ido, Yuzu, Chu, Sonia, Isobe, Jared Thomas, Ng, Andrew Y., Watson-Parris, Duncan
Clouds play a significant role in global temperature regulation through their effect on planetary albedo. Anthropogenic emissions of aerosols can alter the albedo of clouds, but the extent of this effect, and its consequent impact on temperature chan
Externí odkaz:
http://arxiv.org/abs/2401.14486
Publikováno v:
Quantum 8, 1360 (2024)
Rigorous derivations of the approach of individual elements of large isolated systems to a state of thermal equilibrium, starting from arbitrary initial states, are exceedingly rare. This is particularly true for quantum mechanical systems. We demons
Externí odkaz:
http://arxiv.org/abs/2312.14290
Autor:
T. Eidhammer, A. Gettelman, K. Thayer-Calder, D. Watson-Parris, G. Elsaesser, H. Morrison, M. van Lier-Walqui, C. Song, D. McCoy
Publikováno v:
Geoscientific Model Development, Vol 17, Pp 7835-7853 (2024)
This paper documents the methodology and preliminary results from a perturbed parameter ensemble (PPE) technique, where multiple parameters are varied simultaneously and the parameter values are determined with Latin hypercube sampling. This is done
Externí odkaz:
https://doaj.org/article/9cb4791e11df4d82bb6c2f8f899b9398
Emulators, or reduced complexity climate models, are surrogate Earth system models that produce projections of key climate quantities with minimal computational resources. Using time-series modelling or more advanced machine learning techniques, data
Externí odkaz:
http://arxiv.org/abs/2307.10052