Physic Informed Neurosymbolic Network

Physics-informed neurosymbolic networks integrate symbolic reasoning with neural networks to leverage both data-driven learning and pre-existing knowledge, aiming to improve the robustness, explainability, and efficiency of AI systems. Current research focuses on applying these hybrid models to diverse problems, including robotics (motion planning, SLAM), knowledge graph alignment, and control systems, often employing architectures that combine neural networks with symbolic representations like logic or grammars. This approach holds significant promise for enhancing the performance and reliability of AI in safety-critical applications and for bridging the gap between symbolic AI and deep learning.