A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
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This paper provides a comprehensive review of Physics-informed neural networks (PINNs), covering methodologies, applications, and challenges in solving PDE problems.
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This paper introduces self-adaptive physics-informed neural networks that dynamically adjust loss weights during training to improve solution accuracy for nonlinear PDEs.
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This paper introduces convolutional neural operators (CNOs) designed to preserve continuous nature for robust and accurate learning of PDEs.