no code implementations • 23 Feb 2022 • Xihaier Luo, Balasubramanya T. Nadiga, Yihui Ren, Ji Hwan Park, Wei Xu, Shinjae Yoo
Since model bias and associated initialization shock are serious shortcomings that reduce prediction skills in state-of-the-art decadal climate prediction efforts, we pursue a complementary machine-learning-based approach to climate prediction.
no code implementations • 16 Dec 2021 • Xiaoqian Chen, Balasubramanya T. Nadiga, Ilya Timofeyev
In this paper we demonstrate that reservoir computing can be used to learn the dynamics of the shallow-water equations.
no code implementations • 2 Dec 2021 • Maliha Hossain, Balasubramanya T. Nadiga, Oleg Korobkin, Marc L. Klasky, Jennifer L. Schei, Joshua W. Burby, Michael T. McCann, Trevor Wilcox, Soumi De, Charles A. Bouman
Radiography is often used to probe complex, evolving density fields in dynamic systems and in so doing gain insight into the underlying physics.
no code implementations • 27 Aug 2021 • Wei Xu, Xihaier Luo, Yihui Ren, Ji Hwan Park, Shinjae Yoo, Balasubramanya T. Nadiga
From the perspective of climate dynamics, these findings suggest a dominant role for local processes and a negligible role for remote teleconnections at the spatial and temporal scales we consider.
no code implementations • 20 May 2019 • Balasubramanya T. Nadiga, Chiyu Jiang, Daniel Livescu
We focus on improving the accuracy of an approximate model of a multiscale dynamical system that uses a set of parameter-dependent terms to account for the effects of unresolved or neglected dynamics on resolved scales.