Výsledky vyhledávání - "Parashar, Nishant"

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Continuous latent representations for modeling precipitation with deep learning

Autor: Radhakrishnan, Gokul, Sundar, Rahul, Parashar, Nishant, Blanchard, Antoine, Wang, Daiwei, Dodov, Boyko

The sparse and spatio-temporally discontinuous nature of precipitation data presents significant challenges for simulation and statistical processing for bias correction and downscaling. These include incorrect representation of intermittency and ext

Externí odkaz: http://arxiv.org/abs/2412.14620

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TAUDiff: Improving statistical downscaling for extreme weather events using generative diffusion models

Autor: Sundar, Rahul, Parashar, Nishant, Blanchard, Antoine, Dodov, Boyko

Deterministic regression-based downscaling models for climate variables often suffer from spectral bias, which can be mitigated by generative models like diffusion models. To enable efficient and reliable simulation of extreme weather events, it is c

Externí odkaz: http://arxiv.org/abs/2412.13627

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A Multi-Scale Deep Learning Framework for Projecting Weather Extremes

Autor: Blanchard, Antoine, Parashar, Nishant, Dodov, Boyko, Lessig, Christian, Sapsis, Themistoklis

Weather extremes are a major societal and economic hazard, claiming thousands of lives and causing billions of dollars in damage every year. Under climate change, their impact and intensity are expected to worsen significantly. Unfortunately, general

Externí odkaz: http://arxiv.org/abs/2210.12137

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Modelling pressure-Hessian from local velocity gradients information in an incompressible turbulent flow field using deep neural networks

Autor: Parashar, Nishant, Sinha, Sawan S., Srinivasan, Balaji

The understanding of the dynamics of the velocity gradients in turbulent flows is critical to understanding various non-linear turbulent processes. The pressure-Hessian and the viscous-Laplacian govern the evolution of the velocity-gradients and are

Externí odkaz: http://arxiv.org/abs/1911.08056

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Distributed physics informed neural network for data-efficient solution to partial differential equations

Autor: Dwivedi, Vikas, Parashar, Nishant, Srinivasan, Balaji

Publikováno v: Neurocomputing, 420, 299-316

The physics informed neural network (PINN) is evolving as a viable method to solve partial differential equations. In the recent past PINNs have been successfully tested and validated to find solutions to both linear and non-linear partial differenti

Externí odkaz: http://arxiv.org/abs/1907.08967

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