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Author: MarleneBusch

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 # Summary
 
-`artist` is a software package for concentrating solar power (CSP) digital twins, featuring native GPU acceleration, data-parallel processing, and support for distributed computation to ensure scalability. Solar tower power plants use mirrors (heliostats) to reflect and concentrate sunlight onto a small area, called the receiver, located at the top of the solar tower. This Python package offers a high-performance, memory-efficient interface for various optimization tasks and for parameter learning of the power plant components. The ray tracer module within `artist` simulates how light rays interact with the 3D scene and computes flux density distributions based on environmental conditions to ultimately optimize the distribution of the thermal power on the receiver by finding optimal motor positions for each heliostat. All within a single tool, `artist` combines its ray tracing capabilities with functionality to create and optimize physics-informed digital twins of CSP plants. `artist` enhances the accuracy of its ray tracing results by learning real-world parameters of deformed heliostat surfaces and kinematic deviations. Multiple heliostat surfaces, represented through differentiable Non-Uniform Rational B-Splines (NURBS), can be reconstructed in parallel from easily accessible measured flux density distributions. Additionally, `artist` provides calibration algorithms for the kinematic system to account for heliostat alignment errors caused by actuator inaccuracies.
+`artist` is a software package for concentrating solar power (CSP) digital twins, featuring native GPU acceleration, data-parallel processing, and support for distributed computation to ensure scalability. Solar tower power plants use mirrors (heliostats) to reflect and concentrate sunlight onto a small area, called the receiver, located at the top of the solar tower. This Python package offers a high-performance, memory-efficient interface for various optimization tasks and for parameter learning of the power plant components. The ray tracer module within `artist` simulates how light rays interact with the three-dimensional scene and computes flux distributions based on environmental conditions to ultimately optimize the distribution of the thermal power on the receiver by finding optimal alignments for each heliostat. All within a single tool, `artist` combines its ray tracing capabilities with additional functionality to create and optimize physics-informed digital twins of CSP plants. `artist` enhances the accuracy of its ray tracing results by reconstructing real-world parameters of deformed heliostat surfaces and kinematic deviations. Multiple heliostat surfaces, represented through differentiable Non-Uniform Rational B-Splines (NURBS), can be reconstructed in parallel from easily accessible calibration data. Additionally, `artist` provides reconstructions algorithms for the kinematic system to account for heliostat alignment errors in the digital twin, caused by actuator inaccuracies of the real power plant.
 
 # Statement of Need
 
-Concentrating solar power is a sustainable and renewable alternative to fossil fuels and nuclear energy, providing a more environmentally friendly solution to meet the globally rising demand for energy. The absorbed thermal power in a solar tower can be converted into electricity, high-temperature heat for industrial processes, or carbon-neutral fuels. The economic performance of solar tower power plants has yet to reach its full potential, as operational costs remain high due to real-time control requirements, dynamic weather conditions, and temperature constraints. Digital twins with advanced simulation techniques as well as precise behavior analysis and prediction capabilities are essential for establishing fully autonomous power plant operation and a consequential reduction in costs. While solar tower power plants may vary in their individual architectural details, their digital twins consistently rely on ray tracing. Conventional ray tracers [@Ahlbrink:20212], [@SolTrace], [@Tonatiuh:2028] achieve good results in simulating power plant behavior. However, they can only use ray tracing to make predictions based on supplied data and their current model. From a machine learning perspective, these ray tracers are confined to forward computations only, and therefore they often require large amounts of data to function accurately. `artist` addresses this limitation with its differentiable implementation of the ray tracer and all connecting modules. The differentiability significantly improves the data requirements for CSP digital twins and also enables additional applications, including aim point control and optimization, heliostat field layout optimization, and solar tower design optimization for diverse environmental conditions. The underlying concepts of `artist` are based on previous publications [@Pargmann:2024], which have demonstrated the potential of increasing solar tower power plant efficiency. `artist`'s modular architecture, built on abstraction and inheritance, enables its application across diverse solar tower power plant designs. Users can incorporate specific design details and define custom power plant behavior while leveraging shared differentiable algorithms for alignment, ray tracing, heliostat surface reconstruction, and kinematic optimization defined in `artist`. This software is designed for researchers, power plant operators, developers within the CSP community or anyone else interested in the field. `artist` includes data loaders and converters compatible with various data sources, including the open-access CSP database PAINT [@Phipps:2025], for users who do not have direct access to an operational power plant. Overall, the accessibility of the data, the modularity of the software, and its adherence to the FAIR principles for research software [@Barker:2022] aim to strengthen community engagement and collaboration for further research advancements.
+Concentrating solar power is a sustainable and renewable alternative to fossil fuels and nuclear energy, providing a more environmentally friendly solution to meet the globally rising demand for energy. The absorbed thermal power in a solar tower can be converted into electricity or high-temperature heat for industrial processes. The economic performance of solar tower power plants has yet to reach its full potential, as operational costs remain high due to real-time control requirements and dynamic weather conditions. Digital twins with advanced simulation techniques, as well as precise behavior analysis and prediction capabilities are essential for establishing fully autonomous power plant operation and a consequential reduction in costs. While solar tower power plants may vary in their individual architectural details, their digital twins consistently rely on ray tracing. Conventional ray tracers [@Ahlbrink:20212], [@SolTrace], [@Tonatiuh:2028] achieve good results in simulating power plant behavior. However, they can only use ray tracing to make predictions based on supplied data and their current model. From a machine learning perspective, these ray tracers are confined to forward computations, and therefore they often require large amounts of data to function accurately. `artist` addresses this limitation with its differentiable implementation of the ray tracer and all connecting modules. The differentiability significantly improves the data requirements for CSP digital twins and also enables additional applications, including aim point control and optimization, heliostat field layout optimization, and solar tower design optimizations. The underlying concepts of `artist` are based on previous publications [@Pargmann:2024], which have demonstrated the potential of increasing solar tower power plant efficiency. `artist`'s modular architecture, built on abstraction and inheritance, enables its application across diverse solar tower power plant designs. Users can incorporate specific design details and define custom power plant behavior to be used in combination with shared differentiable algorithms for alignment, ray tracing, heliostat surface reconstruction, and kinematic reconstruction already defined in `artist`. This software is designed for researchers, power plant operators, developers within the CSP community or anyone else interested in the field. `artist` includes data loaders compatible with various data sources, including the open-access CSP database PAINT [@Phipps:2025], for users who do not have direct access to an operational power plant. Overall, the accessibility of the data, the modularity of the software, and its adherence to the FAIR principles for research software [@Barker:2022] aim to strengthen community engagement and collaboration for further research advancements.
 
 # Features
 
-The main features of `artist` are shown in Figure \ref{fig:flowchart}. To create digital twins of solar tower power plants in `artist`, users are asked to provide HDF5 files containing data about the physical layout of the power plant. The HDF5 scenarios can be generated by `artist` from various data sources. `artist` unpacks these files to initiate the simulation and optimization process by reconstructing real-world mirror surfaces, aligning heliostats, performing raytracing, predicting flux density distributions and performing calibration routines. The final results, the optimized motor positions for each heliostat, can be used directly as input to a power plant control software. To efficiently handle and learn heliostat surfaces `artist` contains a fully differentiable, parallelized NURBS implementation.
-![Features of `artist` the AI-enhanced differentiable Ray Tracer for Irradiation Prediction in Solar Tower Digital Twins. \label{fig:flowchart}](artist_flowchart.png)
+The main features of `artist` are shown in Figure \ref{fig:flowchart}. To create digital twins of solar tower power plants in `artist`, users are asked to provide HDF5-files containing data about the physical layout of the power plant. The HDF5 scenarios can be generated by `artist` from various data sources. `artist` unpacks these files to initiate the simulation process by aligning heliostats and performing ray tracing to predict flux density distributions. This combination of alignment and ray tracing is used iteratively in the optimization tasks for reconstructing real-world mirror surfaces and the kinematic and for subsequently optimizing the heliostat aim points. The optimized parameters, can be used directly as input to a power plant control software. To efficiently handle heliostat surfaces, `artist` contains a fully differentiable, parallelized NURBS implementation.
+![Features of `artist` the AI-enhanced differentiable Ray Tracer for Irradiation Prediction in Solar Tower Digital Twins. \label{fig:flowchart}](flowchart.png)
 
 # Acknowledgements