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20212026
most citedPhysics Informed Neural Networks (PINNs)for approximating nonlinear dispersive PDEs

2 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

math.NA2026

Super-approximation and optimal convergence of the stabilized BGN method with piecewise linear parametric finite elements for curve-shortening flow

Genming Bai

We develop a bihomotopy argument to estimate the discrete material time derivatives of consistency functionals. Combined with summation-by-parts, this argument yields a directional…

math.NA2026

Stabilization and optimal convergence of Dziuk's method with piecewise linear parametric finite elements for curve-shortening flow

Genming Bai

We propose a stabilized version of the fully discrete Dziuk's method for the curve-shortening flow of a closed planar curve with piecewise linear parametric finite elements. With a…

math.NA2024

Maximal regularity of evolving FEMs for parabolic equations on an evolving surface

Genming Bai, Balázs Kovács, Buyang Li

In this paper, we prove that spatially semi-discrete evolving finite element method for parabolic equations on a given evolving hypersurface of arbitrary dimensions preserves the m…

math.NA2024★ 1 cited

Weak maximum principle of finite element methods for parabolic equations in polygonal domains

Genming Bai, Dmitriy Leykekhman, Buyang Li

The weak maximum principle of finite element methods for parabolic equations is proved for both semi-discretization in space and fully discrete methods with -step backward diffe…

math.NA2021★ 2 cited

Physics Informed Neural Networks (PINNs)for approximating nonlinear dispersive PDEs

Genming Bai, Ujjwal Koley, Siddhartha Mishra +1

We propose a novel algorithm, based on physics-informed neural networks (PINNs) to efficiently approximate solutions of nonlinear dispersive PDEs such as the KdV-Kawahara, Camassa-…