Abstract Inverse rendering aims to infer scene parameters from observed images. In Solar Power Tower (SPT) systems, this corresponds to an aiming optimization problem—adjusting heliostats' orientations to shape the radiative flux density distribution (RFDD) on the receiver to conform to a desired distribution. The SPT system is widely favored in the field of renewable energy, where aiming optimization is crucial for ensuring its thermal efficiency and safety. However, traditional aiming optimization methods are inefficient and fail to meet online demands. In this paper, a novel optimization approach, DiffNEG, is proposed. DiffNEG introduces a differentiable rasterization method to model the reflected radiative flux of each heliostat as an elliptical Gaussian distribution. It leverages data‐driven techniques to enhance simulation accuracy and employs automatic differentiation combined with gradient descent to achieve online, gradient‐guided optimization in a continuous solution space. Experiments on a real large‐scale heliostat field with nearly 30,000 heliostats demonstrate that DiffNEG can optimize within 10 seconds, improving efficiency by one order of magnitude compared to the latest DiffMCRT method and by three orders of magnitude compared to traditional heuristic methods, while also exhibiting superior robustness under both steady and transient state.

Utility Management in a library is the programmatic tool with the synthetic mental program ability, along with Artificial Intelligence capacities, headed to manage a high volume of books, articles, and assignments, which help to ease the manual significance of librarians. This computerized machine code helps librarians to deal with various databases of the library management system. This framework keeps the records of all the resource details in an optimized manner. It uses a utility management software code with an optimized search classifier that helps to deal with the resource of the library. In this work, the Heuristic Utility Management Algorithm (HUMA) has been used to keep track of resources in the library using mathematical modeling and standardized programmatic computation on tags, which relates the decode scanner to parse the input information. HUMA helps to reduce the manual routine work done by the librarians, and it has been analyzed in this research with prominent survey outcomes based on experimental validation.