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Hewlett Packard Enterprise (United States)

United States

10 papers here155 citations across 10
fields
  • cs.DC2
  • cs.LG2
  • astro-ph.SR1
  • cond-mat.mtrl-sci1
  • cs.AI1
  • cs.ET1
  • physics.comp-ph1
  • physics.optics1
ROR 020x0c621OpenAlex

affiliations via OpenAlex

output
20172024
most citedInverse Design of Grating Couplers Using the Policy Gradient Method from Reinforcement Learning

37 citations

researchers with a paper here
  • Ashwin Ramesh Babu4
  • Avisek Naug4
  • Sahand Ghorbanpour4
  • Soumyendu Sarkar4
  • Vineet Gundecha4
  • Alexander Shmakov2
  • Antonio Guillén2
  • Sajad Mousavi2
  • Aashaka Shah1
  • Aayush Ankit1 · h 22
  • Adam Schwartzberg1
  • A. F. Pérez Sánchez1
collaborating institutions
  • Hewlett-Packard (United States)US3 papers
  • Lawrence Berkeley National LaboratoryUS2 papers
  • University of California, BerkeleyUS2 papers
  • American University of BeirutLB1 paper
  • Association for Computing MachineryUS1 paper
  • Center for Astrophysics Harvard & SmithsonianUS1 paper
  • Centre National de la Recherche ScientifiqueFR1 paper
  • Chalmers University of TechnologySE1 paper
  • Clean Energy (United States)US1 paper
  • European Southern ObservatoryCL1 paper
  • Hewlett-Packard (United Kingdom)GB1 paper
  • Jet Propulsion LaboratoryUS1 paper
Showing physics.comp-phShow all

1 paper · 1 filter

physics.comp-ph2021★ 37 cited

Inverse Design of Grating Couplers Using the Policy Gradient Method from Reinforcement Learning

Sean Hooten, Raymond G. Beausoleil, Thomas Van Vaerenbergh

We present a proof-of-concept technique for the inverse design of electromagnetic devices motivated by the policy gradient method in reinforcement learning, named PHORCED (PHotonic…

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