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K. Shukla

28 papers hereh-index 202.5k citations53 works total

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

author position
  • first author10
  • middle author18

Across the 28 of 28 papers where every author was matched, so the position is known.

fields
  • cs.LG10
  • physics.flu-dyn6
  • math.NA3
  • cond-mat.mtrl-sci2
  • q-bio.QM2
  • cond-mat.dis-nn1
same name
  • K. Shukla — 2 papers, h 27
  • K. Shukla — 1 paper, h 19
  • K. Shukla — 1 paper, h 3
  • K. Shukla — 1 paper, h 3

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

activity
20192025
most citedPhysics-informed Neural Networks (PINNs) for Wave Propagation and Full Waveform Inversions

440 citations · 851 across the 27 of their papers we have counts for

collaborators
Showing 2024Show all

3 papers · 1 filter

cs.LG2024

NeuroSEM: A hybrid framework for simulating multiphysics problems by coupling PINNs and spectral elements

Khemraj Shukla, Zongren Zou, Chi Hin Chan +3

Multiphysics problems that are characterized by complex interactions among fluid dynamics, heat transfer, structural mechanics, and electromagnetics, are inherently challenging due…

cs.LG2024★ 8 cited

A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks

Khemraj Shukla, Juan Diego Toscano, Zhicheng Wang +2

Kolmogorov-Arnold Networks (KANs) were recently introduced as an alternative representation model to MLP. Herein, we employ KANs to construct physics-informed machine learning mode…

physics.flu-dyn2024

Deep operator learning-based surrogate models for aerothermodynamic analysis of AEDC hypersonic waverider

Khemraj Shukla, Jasmine Ratchford, Luis Bravo +4

Neural networks are universal approximators that traditionally have been used to learn a map between function inputs and outputs. However, recent research has demonstrated that dee…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.