Poisson equation

The 'Poisson equation' dataset/benchmark is used to evaluate the performance of physics-informed neural networks (PINNs) in solving forward and inverse partial differential equations (PDEs) that exhibit sharp solutions.

Papers using Poisson equation (9)

From Theory to Application: A Practical Introduction to Neural Operators in Scientific Computing2025 · 5 citesSeparated-Variable Spectral Neural Networks: A Physics-Informed Learning Approach for High-Frequency PDEs2025 · 2 citesQuantum Neural Physics: Solving Partial Differential Equations on Quantum Simulators using Quantum Convolutional Neural Networks2026DInf-Grid: A Neural Differential Equation Solver with Differentiable Feature Grids2026Evidential Physics-Informed Neural Networks for Scientific Discovery2025Employing Deep Neural Operators for PDE control by decoupling training and optimization2025KHRONOS: a Kernel-Based Neural Architecture for Rapid, Resource-Efficient Scientific Computation2025Improving physics-informed neural networks with meta-learned optimization2023 · 11 citesPhysics-informed neural networks with residual/gradient-based adaptive sampling methods for solving PDEs with sharp solutions2023 · 5 cites