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Florian Schäfer

14 papers hereh-index 12581 citations19 works total

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

author position
  • sole author1
  • first author5
  • middle author4
  • last author4

Across the 14 of 14 papers where every author was matched, so the position is known.

fields
  • math.NA7
  • cs.LG2
  • cs.CE1
  • math.OC1
  • math.ST1
  • physics.comp-ph1
same name
  • Florian Schäfer — 6 papers, h 1
  • Florian Schäfer — 2 papers, h 2
  • Florian Schäfer — 1 paper
  • Florian Schäfer — 1 paper, h 2

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
20172026
most citedCompetitive Gradient Descent

45 citations · 77 across the 11 of their papers we have counts for

collaborators
Showing 2023Show all

4 papers · 1 filter

math.NA2023

Information geometric regularization of the barotropic Euler equation

Ruijia Cao, Florian Schäfer

Shock waves in gas dynamics feature jump discontinuities that hinder numerical simulations. Viscous regularizations are prone to excessive dissipation of fine-scale structures. In…

physics.comp-ph2023★ 3 cited

Fast Macroscopic Forcing Method

Spencer H. Bryngelson, Florian Schäfer, Jessie Liu +1

The macroscopic forcing method (MFM) of Mani and Park and similar methods for obtaining turbulence closure operators, such as the Green's function-based approach of Hamba, recover…

math.ST2023

Score Operator Newton transport

Nisha Chandramoorthy, Florian Schaefer, Youssef Marzouk

We propose a new approach for sampling and Bayesian computation that uses the score of the target distribution to construct a transport from a given reference distribution to the t…

stat.ML2023

Variational sparse inverse Cholesky approximation for latent Gaussian processes via double Kullback-Leibler minimization

Jian Cao, Myeongjong Kang, Felix Jimenez +3

To achieve scalable and accurate inference for latent Gaussian processes, we propose a variational approximation based on a family of Gaussian distributions whose covariance matric…

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