MUMBAI, India, Sept. 14 -- Intellectual Property India has published a patent application (202611086920 A) filed by Mr. Aman Deep Vishwkarma; Dr. Alok Sagar Gautam; Mr. Ankit Kumar; Mr. Shyam Narayan Nautiyal; Dr. Karan Singh; Dr. Sanjeev Kumar; and Ms. Saraswati on July 16, 2026, for A System And Method For Atmospheric Energy Transfer Analysis Using Physics-Informed Artificial Intelligence.
Inventors include Mr. Aman Deep Vishwkarma; Dr. Alok Sagar Gautam; Mr. Ankit Kumar; Mr. Shyam Narayan Nautiyal; Dr. Karan Singh; Dr. Sanjeev Kumar; and Ms. Saraswati.
The application for the patent was published on September 11, 2026, under issue no. 37/2026.
Abstract: The present invention relates to a system and method for analyzing atmospheric energy transfer using physics-informed artificial intelligence. The system includes a distributed atmospheric sensing module, an adaptive sensor confidence optimization module, a multi-layer atmospheric coupling module, a physics-guided energy reconstruction engine, a hidden energy exchange identification module, an atmospheric energy conservation verification module, an uncertainty quantification module, an uncertainty-driven autonomous data acquisition controller, an adaptive physics parameter calibration module, an atmospheric energy anomaly identification module, an explainable atmospheric energy interpretation module, an atmospheric energy memory matrix, and one or more processors and memory. The revealed system reconstructs atmospheric energy transfer pathways by combining multi-source atmospheric observations with embedded physical conservation constraints, dynamically evaluates sensor reliability, estimates hidden atmospheric energy exchanges, verifies physical consistency of reconstructed energy states, and autonomously seeks additional observations based on prediction uncertainty. The system also generates explainable analytical outputs and stores validated atmospheric energy transfer signatures for future analysis. The present invention improves analytical accuracy, physical consistency, computational robustness, adaptive sensing efficiency and decision-support capability for atmospheric energy transfer analysis.
Disclaimer: Curated by HT Syndication.
