Scott Armstrong

Professor of Mathematics
Courant Institute of Mathematical Sciences
New York University
251 Mercer St
New York, NY 10012

email: scotta@cims.nyu.edu
office: WWH 530

Publications and preprints

Curriculum vitae

Google scholar profile

Mathscinet profile

Book

  • S. Armstrong, T. Kuusi and J.-C. Mourrat. Quantitative Stochastic Homogenization and Large-Scale Regularity. Grundlehren der mathematischen Wissenschaften (A Series of Comprehensive Studies in Mathematics), Vol. 352 (2019), xxxviii+518p. download
    • (The arXiv version is outdated and no longer supported---read at your own risk!)

Selected papers

  • S. Armstrong and W. Wu. $C^2$ regularity of the surface tension for the $\nabla\phi$ interface model. arXiv
  • S. Armstrong, S. Ferguson and T. Kuusi. Homogenization, linearization and large-scale regularity for nonlinear elliptic equations. arXiv
  • S. Armstrong and P. Dario. Elliptic regularity and quantitative homogenization on percolation clusters. Comm. Pure Appl. Math., 71 (2018), 1717-1849. arXiv | journal
  • S. Armstrong, T. Kuusi and J.-C. Mourrat. The additive structure of elliptic homogenization. Invent. Math., 208 (2017), 999-1154. arXiv | journal
  • S. Armstrong and P. Cardaliaguet. Stochastic homogenization of quasilinear Hamilton-Jacobi equations and geometric motions. J. Eur. Math. Soc., 20 (2018), 797-864. arXiv | journal
  • S. N. Armstrong and C. K. Smart. Quantitative stochastic homogenization of convex integral functionals. Ann. Sci. Éc. Norm. Supér., 48 (2016) 423-481. arXiv | journal.
    ★ This paper received the 2017 SIAG/APDE Prize for most outstanding paper in PDE.
  • S. N. Armstrong and C. K. Smart. Quantitative stochastic homogenization of elliptic equations in nondivergence form, Arch. Ration. Mech. Anal., 214 (2014), 867-911. arXiv | journal
  • S. N. Armstrong, P. Cardaliaguet and P. E. Souganidis. Error estimates and convergence rates for the stochastic homogenization of Hamilton-Jacobi equations, J. Amer. Math. Soc., 27 (2014), 479-540. arXiv | journal