Zobrazeno 1 - 10
of 80
pro vyhledávání: '"Nadiga, Balasubramanya T."'
Autor:
Gautam, Siddhant, Klasky, Marc L., Nadiga, Balasubramanya T., Wilcox, Trevor, Salazar, Gary, Ravishankar, Saiprasad
Density reconstruction from X-ray projections is an important problem in radiography with key applications in scientific and industrial X-ray computed tomography (CT). Often, such projections are corrupted by unknown sources of noise and scatter, whi
Externí odkaz:
http://arxiv.org/abs/2408.12766
A trained attention-based transformer network can robustly recover the complex topologies given by the Richtmyer-Meshkoff instability from a sequence of hydrodynamic features derived from radiographic images corrupted with blur, scatter, and noise. T
Externí odkaz:
http://arxiv.org/abs/2408.00985
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. The e
Externí odkaz:
http://arxiv.org/abs/2202.11244
In this paper we demonstrate that reservoir computing can be used to learn the dynamics of the shallow-water equations. In particular, while most previous applications of reservoir computing have required training on a particular trajectory to furthe
Externí odkaz:
http://arxiv.org/abs/2112.09182
Autor:
Hossain, Maliha, Nadiga, Balasubramanya T., Korobkin, Oleg, Klasky, Marc L., Schei, Jennifer L., Burby, Joshua W., McCann, Michael T., Wilcox, Trevor, De, Soumi, Bouman, Charles A.
Publikováno v:
Opt. Express, vol. 30, no. 9, pp. 14432-14452, Apr. 2022
Radiography is often used to probe complex, evolving density fields in dynamic systems and in so doing gain insight into the underlying physics. This technique has been used in numerous fields including materials science, shock physics, inertial conf
Externí odkaz:
http://arxiv.org/abs/2112.01627
We present a study using a class of post-hoc local explanation methods i.e., feature importance methods for "understanding" a deep learning (DL) emulator of climate. Specifically, we consider a multiple-input-single-output emulator that uses a DenseN
Externí odkaz:
http://arxiv.org/abs/2108.13203
We show that in addition to providing effective and competitive closures, when analysed in terms of dynamics and physically-relevant diagnostics, artificial neural networks (ANNs) can be both interpretable and provide useful insights in the on-going
Externí odkaz:
http://arxiv.org/abs/2004.07207
Autor:
Portwood, Gavin D., Mitra, Peetak P., Ribeiro, Mateus Dias, Nguyen, Tan Minh, Nadiga, Balasubramanya T., Saenz, Juan A., Chertkov, Michael, Garg, Animesh, Anandkumar, Anima, Dengel, Andreas, Baraniuk, Richard, Schmidt, David P.
Fluid turbulence is characterized by strong coupling across a broad range of scales. Furthermore, besides the usual local cascades, such coupling may extend to interactions that are non-local in scale-space. As such the computational demands associat
Externí odkaz:
http://arxiv.org/abs/1911.05180
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. We start by considering variou
Externí odkaz:
http://arxiv.org/abs/1905.08227
Anomaly-diffusing energy balance models (AD-EBM) are routinely employed to analyze and emulate the warming response of both observed and simulated Earth systems. We demonstrate a deficiency in common multi-layer as well as continuous-diffusion AD-EBM
Externí odkaz:
http://arxiv.org/abs/1902.10836