Citation :

Subhasis Kundu, 2026. "Physics-Informed Intelligence Engines: Embedding Physical Laws into AI for Real-World Reasoning and Control " ESP International Journal of Artificial Intelligence & Data Science [IJAIDS]  Volume 2, Issue 2: 01-08.

Abstract :

Physics-informed intelligence engines are artificial intelligence systems that leverage on fundamental physical principles to reinforce. Reasoning and control in the real world. Training AI systems with canonical physics and simulation models lets for science-Level reasoning, resulting in accurate predictions and solid decision making. This strategy connects the two worlds of data-driven AI and in-depth expertise, improving interpretability and generalization within multifaceted ecosystems. This paper investigates architectural designs, learning frameworks and control strategies that use physics-informed constraints. Numerous case studies have shown the effectiveness of physics-informed AI in robotics, autonomous systems, and environmental modeling. The solutions, challenges like computational challenge and model integration are also discussed. Physicing + AI conger to an advent of intelligent engines that can perform scientific discovery, and operate reliably in dynamic environments.

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Keywords :

Physics-Informed AI, First-Principles Physics, Simulation, Scientific Reasoning, Real-World Control, Intelligent Systems, AI Architecture.