◍wovepaper
SearchResearchersInstitutions
Sign in
researcher

R. Singh

3 papers hereh-index 4201 citations7 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • last author3

Across the 3 of 3 papers where every author was matched, so the position is known.

fields
  • physics.flu-dyn3
same name
  • R. Singh — 120 papers
  • R. Singh — 81 papers
  • R. Singh — 75 papers
  • R. Singh — 29 papers, h 22
  • R. Singh — 27 papers, h 13
  • R. Singh — 25 papers, h 19

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedA comparative study of various Deep Learning techniques for spatio-temporal Super-Resolution reconstruction of Forced Isotropic Turbulent flows

3 citations · 7 across the 3 of their papers we have counts for

collaborators

3 papers

physics.flu-dyn2023★ 2 cited

A Synergistic Framework Leveraging Autoencoders and Generative Adversarial Networks for the Synthesis of Computational Fluid Dynamics Results in Aerofoil Aerodynamics

Tanishk Nandal, Vaibhav Fulara, Raj Kumar Singh

In the realm of computational fluid dynamics (CFD), accurate prediction of aerodynamic behaviour plays a pivotal role in aerofoil design and optimization. This study proposes a nov…

physics.flu-dyn2021★ 2 cited

Parameterization of Forced Isotropic Turbulent Flow using Autoencoders and Generative Adversarial Networks

Kanishk, Tanishk Nandal, Prince Tyagi +1

Autoencoders and generative neural network models have recently gained popularity in fluid mechanics due to their spontaneity and low processing time instead of high fidelity CFD s…

physics.flu-dyn2021★ 3 cited

A comparative study of various Deep Learning techniques for spatio-temporal Super-Resolution reconstruction of Forced Isotropic Turbulent flows

T. S. Sachin Venkatesh, Rajat Srivastava, Pratyush Bhatt +2

Super-resolution is an innovative technique that upscales the resolution of an image or a video and thus enables us to reconstruct high-fidelity images from low-resolution data. Th…

◍wovepaper

Papers, researchers and institutions, woven together.

Explore
  • Search
  • Researchers
  • Institutions
Account
  • Library
  • Chat
Data
  • arXiv.org
  • Semantic Scholar
  • OpenAlex
  • Latest RSS
AboutContactPrivacyDevelopersllms.txtopenapi.json
Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.