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20242026
most citedThe Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS)

1 citations · 1 across the 5 of their papers we have counts for

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math.NA2026

Efficient Explicit Taylor ODE Integrators with Symbolic-Numeric Computing

Songchen Tan, Oscar Smith, Christopher Rackauckas

Taylor series methods show a newfound promise for the solution of non-stiff ordinary differential equations (ODEs) given the rise of new compiler-enhanced techniques for calculatin…

math.NA2025

Differentiable Programming for Differential Equations: A Review

Facundo Sapienza, Jordi Bolibar, Frank Schäfer +8

The differentiable programming paradigm is a cornerstone of modern scientific computing. It refers to numerical methods for computing the gradient of a numerical model's output. Ma…

math.NA2025

A Fully Adaptive Radau Method for the Efficient Solution of Stiff Ordinary Differential Equations at Low Tolerances

Shreyas Ekanathan, Oscar Smith, Christopher Rackauckas

Radau IIA methods, specifically the adaptive order Radau method in Fortran due to Hairer, are known to be state-of-the-art for the high-accuracy solution of highly stiff ordinary d…

math.NA2025

NonlinearSolve.jl: High-Performance and Robust Solvers for Systems of Nonlinear Equations in Julia

Avik Pal, Flemming Holtorf, Axel Larsson +6

Efficiently solving nonlinear equations underpins numerous scientific and engineering disciplines, yet scaling these solutions for challenging system models remains a challenge. Th…

math.NA2025

Scalable higher-order nonlinear solvers via higher-order automatic differentiation

Songchen Tan, Keming Miao, Alan Edelman +1

This paper demonstrates new methods and implementations of nonlinear solvers with higher-order of convergence, which is achieved by efficiently computing higher-order derivatives.…