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S. Nair

4 papers hereh-index 6134 citations18 works total

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

author position
  • first author3
  • middle author1

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • physics.comp-ph2
  • cs.CE1
  • cs.LG1
same name
  • S. Nair — 27 papers, h 16
  • S. Nair — 4 papers, h 13
  • S. Nair — 4 papers, h 14
  • S. Nair — 4 papers, h 2
  • S. Nair — 3 papers, h 11
  • S. Nair — 3 papers, h 1

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

collaborators

4 papers

physics.comp-ph2025

Reinforcement learning framework for the mechanical design of microelectronic components under multiphysics constraints

Siddharth Nair, Timothy F. Walsh, Greg Pickrell +1

This study focuses on the development of reinforcement learning based techniques for the design of microelectronic components under multiphysics constraints. While traditional desi…

cs.CE2024

Time transient Simulations via Finite Element Network Analysis: Theoretical Formulation and Numerical Validation

Mehdi Jokar, Siddharth Nair, Fabio Semperlotti

This paper extends the finite element network analysis (FENA) to include a dynamic time-transient formulation. FENA was initially formulated in the context of the linear static ana…

cs.LG2024

Physics and geometry informed neural operator network with application to acoustic scattering

Siddharth Nair, Timothy F. Walsh, Greg Pickrell +1

In this paper, we introduce a physics and geometry informed neural operator network with application to the forward simulation of acoustic scattering. The development of geometry i…

physics.comp-ph2024

Multiple scattering simulation via physics-informed neural networks

Siddharth Nair, Timothy F. Walsh, Greg Pickrell +1

This work presents a physics-driven machine learning framework for the simulation of acoustic scattering problems. The proposed framework relies on a physics-informed neural networ…

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