most citedMulti-soliton solutions and data-driven discovery of higher-order Burgers' hierarchy equations with physics informed neural networks

9 citations · 9 across the 1 of their papers we have counts for

collaborators

2 papers

physics.plasm-ph2026

Physics-Informed Kolmogorov-Arnold Networks for Grad-Shafranov Tokamak Equilibria

D. A. Kaltsas, A. Kuiroukidis, J. Liu +2

We employ equation-driven, physics-constrained deep learning to solve the fixed-boundary Grad-Shafranov (GS) equilibrium problem, constructing axisymmetric magnetohydrodynamic equi…

physics.comp-ph2024★ 9 cited

Multi-soliton solutions and data-driven discovery of higher-order Burgers' hierarchy equations with physics informed neural networks

D. A. Kaltsas, L. Magafas, P. Papadopoulou +1

The Burgers hierarchy consists of nonlinear evolutionary partial differential equations (PDEs) with progressively higher-order dispersive and nonlinear terms. Notable members of th…