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Ajeet Singh

Publications and source records attributed to Ajeet Singh.

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Lyapunov Stability and Optimal Error Estimates for an SIPG Method for Weakly Damped Semilinear Wave Equations

We develop and analyze a fully discrete scheme for the weakly damped semilinear wave equation that combines a Symmetric Interior Penalty Discontinuous Galerkin (SIPG) spatial discretization with a hybrid Crank--Nicolson/second-order Backward Differentiation Formula (CN--BDF2) time integrator. A chord-slope linearization of the nonlinear reaction term is employed, which preserves an exact discrete gradient structure and, crucially, requires {no global Lipschitz continuity assumption} on the nonlinearity. Stability of the fully discrete solution is established through a Lyapunov-based analysis-rather than spectral arguments-by constructing a discrete Lyapunov functional that yields existence, uniqueness, and uniform boundedness of the numerical solution. Under standard regularity assumptions, optimal a~priori error estimates of order $\mathcal{O}(h^{k}+τ^{2})$ in the DG energy norm and $\mathcal{O}(h^{k+1}+τ^{2})$ in the $L^{2}$-norm are proved, where $h$ is the mesh size, $τ$ the time step, and $k$ the polynomial degree. Numerical experiments on two-dimensional problems with linear, cubic, and trigonometric nonlinearities confirm the theoretical convergence rates and illustrate the long-time energy-dissipation properties guaranteed by the Lyapunov structure.

math.NA

PINNs Algorithmic Framework for Simulation of Nonlinear Burgers' Type Models

In this work, a physics-informed neural networks (PINNs) based algorithm is used for simulation of nonlinear 1D and 2D Burgers' type models. This scheme relies on a neural network built to approximate the problem solution and use a trial function that meets the initial data and boundary criteria. First of all, a brief mathematical formulation of the problem and the structure of PINNs, including the neural network architecture, loss construction, and training methodology is described. Finally, the algorithm is demonstrated with five test problems involving variations of the 1D coupled, 2D single and 2D coupled Burgers' models. We compare the PINN-based solutions with exact results to assess accuracy and convergence of the developed algorithm. The results demonstrate that PINNs may faithfully replicate nonlinear PDE solutions and offer competitive performance in terms of inaccuracy and flexibility. This work demonstrates the potential of PINNs as a reliable approach to solving complex time-dependent PDEs.

cs.LG