2 citations · 4 across the 4 of their papers we have counts for
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math.NA2024
Quantifying uncertainty in the numerical integration of evolution equations based on Bayesian isotonic regression
Yuto Miyatake, Kaoru Irie, Takeru Matsuda
This paper presents a new Bayesian framework for quantifying discretization errors in numerical solutions of ordinary differential equations. By modelling the errors as random vari…
math.NA2020★ 2 cited
Generalization of partitioned Runge--Kutta methods for adjoint systems
Takeru Matsuda, Yuto Miyatake
This study computes the gradient of a function of numerical solutions of ordinary differential equations (ODEs) with respect to the initial condition. The adjoint method computes t…
math.NA2019
Adjoint-based exact Hessian computation
Shin-ichi Ito, Takeru Matsuda, Yuto Miyatake
We consider a scalar function depending on a numerical solution of an initial value problem, and its second-derivative (Hessian) matrix for the initial value. The need to extract t…