Classification of chaotic time series with deep learning. - Nicolas Boullé; Vassilios Dallas; Yuji Nakatsukasa; D. Samaddar (CoRR, 2019)

Rational neural networks. - Nicolas Boullé; Yuji Nakatsukasa; Alex Townsend (NeurIPS, 2020)

Bifurcation analysis of stationary solutions of two-dimensional coupled Gross-Pitaevskii equations using deflated continuation. - Efstathios G. Charalampidis; Nicolas Boullé; Patrick E. Farrell; Panayotis G. Kevrekidis (Commun. Nonlinear Sci. Numer. Simul., 2020)

Deflation-based Identification of Nonlinear Excitations of the 3D Gross-Pitaevskii equation. - Nicolas Boullé; Efstathios G. Charalampidis; Patrick E. Farrell; Panayotis G. Kevrekidis (CoRR, 2020)

Computing with Functions in the Ball. - Nicolas Boullé; Alex Townsend (SIAM J. Sci. Comput., 2020)

Accurate numerical simulation of electrodiffusion and water movement in brain tissue. - Ada J. Ellingsrud; Nicolas Boullé; Patrick E. Farrell; Marie E. Rognes (CoRR, 2021)

Bifurcation analysis of two-dimensional Rayleigh-Bénard convection using deflation. - Nicolas Boullé; Vassilios Dallas; Patrick E. Farrell (CoRR, 2021)

An optimal complexity spectral method for Navier-Stokes simulations in the ball. - Nicolas Boullé; Jonasz Slomka; Alex Townsend (CoRR, 2021)

Data-driven discovery of physical laws with human-understandable deep learning. - Nicolas Boullé; Christopher J. Earls; Alex Townsend (CoRR, 2021)

A generalization of the randomized singular value decomposition. - Nicolas Boullé; Alex Townsend (ICLR, 2022)

Optimal control of Hopf bifurcations. - Nicolas Boullé; Patrick E. Farrell; Marie E. Rognes (CoRR, 2022)

Learning Green's functions associated with parabolic partial differential equations. - Nicolas Boullé; Seick Kim; Tianyi Shi; Alex Townsend (CoRR, 2022)

Two-Component 3D Atomic Bose-Einstein Condensates Support Complex Stable Patterns. - Nicolas Boullé; I. Newell; Patrick E. Farrell; Panayotis G. Kevrekidis (CoRR, 2022)

Data-driven discovery of Green's functions. - Nicolas Boullé (CoRR, 2022)

Principled interpolation of Green's functions learned from data. - Harshwardhan Praveen; Nicolas Boullé; Christopher J. Earls (CoRR, 2022)

Bifurcation analysis of a two-dimensional magnetic Rayleigh-Bénard problem. - Fabian Laakmann; Nicolas Boullé (CoRR, 2022)

Learning Green's functions associated with time-dependent partial differential equations. - Nicolas Boullé; Seick Kim; Tianyi Shi; Alex Townsend (J. Mach. Learn. Res., 2022)

Control of Bifurcation Structures using Shape Optimization. - Nicolas Boullé; Patrick E. Farrell; Alberto Paganini (SIAM J. Sci. Comput., 2022)

Elliptic PDE learning is provably data-efficient. - Nicolas Boullé; Diana Halikias; Alex Townsend (CoRR, 2023)

A Mathematical Guide to Operator Learning. - Nicolas Boullé; Alex Townsend (CoRR, 2023)

Learning Elliptic Partial Differential Equations with Randomized Linear Algebra. - Nicolas Boullé; Alex Townsend (Found. Comput. Math., 2023)

Optimization of Hopf Bifurcation Points. - Nicolas Boullé; Patrick E. Farrell; Marie E. Rognes (SIAM J. Sci. Comput., 2023)

LLMs learn governing principles of dynamical systems, revealing an in-context neural scaling law. - Toni J. B. Liu; Nicolas Boullé; Raphaël Sarfati; Christopher J. Earls (EMNLP, 2024)

On the Convergence of Hermitian Dynamic Mode Decomposition. - Nicolas Boullé; Matthew J. Colbrook (CoRR, 2024)

Operator learning without the adjoint. - Nicolas Boullé; Diana Halikias; Samuel E. Otto; Alex Townsend (CoRR, 2024)

Randomized Nyström approximation of non-negative self-adjoint operators. - David Persson; Nicolas Boullé; Daniel Kressner (CoRR, 2024)

Structure-Preserving Operator Learning. - Nacime Bouziani; Nicolas Boullé (CoRR, 2024)

Large Language Models as Markov Chains. - Oussama Zekri; Ambroise Odonnat; Abdelhakim Benechehab; Linus Bleistein; Nicolas Boullé; Ievgen Redko (CoRR, 2024)

Operator learning regularization for macroscopic permeability prediction in dual-scale flow problem. - Christina Runkel; Sinan Xiao; Nicolas Boullé; Yang Chen (CoRR, 2024)

Multivariate Rational Approximation of Functions with Curves of Singularities. - Nicolas Boullé; Astrid Herremans; Daan Huybrechs (SIAM J. Sci. Comput., 2024)

Density estimation with LLMs: a geometric investigation of in-context learning trajectories. - Toni J. B. Liu; Nicolas Boullé; Raphaël Sarfati; Christopher J. Earls (ICLR, 2025)

Lines of Thought in Large Language Models. - Raphaël Sarfati; Toni J. B. Liu; Nicolas Boullé; Christopher J. Earls (ICLR, 2025)

Multiplicative Dynamic Mode Decomposition. - Nicolas Boullé; Matthew J. Colbrook (SIAM J. Appl. Dyn. Syst., 2025)

Fine-Tuning Discrete Diffusion Models with Policy Gradient Methods. - Oussama Zekri; Nicolas Boullé (CoRR, 2025)

Multi-Level Monte Carlo Training of Neural Operators. - James Rowbottom; Stefania Fresca; Pietro Lio; Carola-Bibiane Schönlieb; Nicolas Boullé (CoRR, 2025)

What's in a prompt? Language models encode literary style in prompt embeddings. - Raphaël Sarfati; Haley Moller; Toni J. B. Liu; Nicolas Boullé; Christopher J. Earls (CoRR, 2025)

Convergent Methods for Koopman Operators on Reproducing Kernel Hilbert Spaces. - Nicolas Boullé; Matthew J. Colbrook; Gustav Conradie (CoRR, 2025)