A First Course in the Numerical Analysis of Differential Equations: Gaussian elimination for sparse linear equations

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HiPPO: Recurrent Memory with Optimal Polynomial Projections (2020, methodology): “8The LMU is only known to be fast with the simple forward Euler discretization [71], but not with more sophisticated methods such as bilinear and ZOH that are required to reduce numerical errors for this task.”
Introduction to Numerical Methods in Differential Equations (2006, background): “This is not pursued here, because the matrix approach is limited in its applicability to other types of partial differential equations, specifically, wave equations, and an interesting discussion of this can be found in Iserles [1996]. There is a third approach one can use for establishing stability”
Modeling the Dynamics of PDE Systems with Physics-Constrained Deep Auto-Regressive Networks (2019, background): “Alternatively, when predicting large time-steps with implicit methods the discrete time integration can become the primary source of error due to numerical diffusion [43].”
Simplified State Space Layers for Sequence Modeling (2022, methodology): “We refer the reader to Iserles [25] for more information on discretization methods.”