skip to content

Faculty of Mathematics

 

I am a Research Fellow in Numerical Analysis.

My research interests lie at the intersection between numerical analysis and deep learning. I primarily focus on the mathematical foundations of deep learning to discover mathematical models (partial differential equations) from data, and the development of novel and theoretically justified numerical techniques.

I am a member of the Scientific Artificial Intelligence (SciAI) Center supported by the Office of Naval Research (ONR).

Publications

Operator learning without the adjoint
N Boullé, D Halikias, SE Otto, A Townsend
(2024)
On the Convergence of Hermitian Dynamic Mode Decomposition
N Boullé, MJ Colbrook
(2024)
A Mathematical Guide to Operator Learning
N Boullé, A Townsend
(2023)
Multivariate rational approximation of functions with curves of singularities
N Boullé, A Herremans, D Huybrechs
(2023)
Elliptic PDE learning is provably data-efficient
N Boullé, D Halikias, A Townsend
– Proc Natl Acad Sci U S A
(2023)
120,
e2303904120
Principled interpolation of Green's functions learned from data
H Praveen, N Boullé, C Earls
– Computer Methods in Applied Mechanics and Engineering
(2023)
409,
115971
Elliptic PDE learning is provably data-efficient
N Boullé, D Halikias, A Townsend
(2023)
Two-component three-dimensional atomic Bose-Einstein condensates supporting complex stable patterns
N Boullé, I Newell, PE Farrell, PG Kevrekidis
– Physical Review A
(2023)
107,
012813
Bifurcation analysis of a two-dimensional magnetic Rayleigh-Bénard problem
F Laakmann, N Boullé
(2022)
Principled interpolation of Green's functions learned from data
H Praveen, N Boulle, C Earls
(2022)
  • 1 of 4
  • >

Research Group

Cambridge Image Analysis

Room

F2.05

Personal homepage