Allen-Cahn

The 'Allen-Cahn' dataset/benchmark is used to evaluate the performance of physics-informed neural networks (PINNs) in solving nonlinear partial differential equations.

Papers using Allen-Cahn (12)

Optimizing the Optimizer for Physics-Informed Neural Networks and Kolmogorov-Arnold Networks2025 · 29 citesAnant-Net: Breaking the Curse of Dimensionality with Scalable and Interpretable Neural Surrogate for High-Dimensional PDEs2025 · 6 citesA Conformal Prediction Framework for Uncertainty Quantification in Physics-Informed Neural Networks2025 · 2 citesHAMNO: A Hierarchical Adaptive Multi-scale Neural Operator with Physics-Informed Learning for Dynamical Systems2026Do physics-informed neural networks (PINNs) need to be deep? Shallow PINNs using the Levenberg-Marquardt algorithm2026A Residual Guided strategy with Generative Adversarial Networks in training Physics-Informed Transformer Networks2025PDE-aware Optimizer for Physics-informed Neural Networks2025Exact Enforcement of Temporal Continuity in Sequential Physics-Informed Neural Networks2024 · 26 citesCan Physics-Informed Neural Networks beat the Finite Element Method?2023 · 11 citesDEQGAN: Learning the Loss Function for PINNs with Generative Adversarial Networks2022 · 6 citesHigh Precision Differentiation Techniques for Data-Driven Solution of Nonlinear PDEs by Physics-Informed Neural Networks2022Going Deeper with Five-point Stencil Convolutions for Reaction-Diffusion Equations2023