Klein-Gordon

The Klein-Gordon dataset/benchmark is used to evaluate the performance of neural network models in solving multi-scale and high-frequency partial differential equations (PDEs).

Papers using Klein-Gordon (5)

QCPINN: Quantum-Classical Physics-Informed Neural Networks for Solving PDEs2025 · 14 citesArchitecture-Optimization Co-Design for Physics-Informed Neural Networks Via Attentive Representations and Conflict-Resolved Gradients2026Synergizing Kolmogorov-Arnold Networks with Dynamic Adaptive Weighting for High-Frequency and Multi-Scale PDE Solutions2025A Residual Guided strategy with Generative Adversarial Networks in training Physics-Informed Transformer Networks2025Discovering Physics-Informed Neural Networks Model for Solving Partial Differential Equations through Evolutionary Computation2024 · 8 cites