update to new joss scope and format

Author: MarleneBusch

joss_paper/paper.bib

@@ -100,3 +100,60 @@ @article{Huang:2021
   address   = {Switzerland},
   issn      = {1424-8220},
 }
+
+@article{Kuhl:2024,
+    publisher = {Elsevier},
+    pages = {112894--1},
+    month = {November},
+    series = {Elsevier},
+    number = {282},
+    title = {Flux Density Distribution Forecasting in Concentrated Solar Tower Plants: A Data-Driven Approach},
+    journal = {Solar Energy},
+    author = {Kuhl, Mathias and Pargmann, Max and Cherti, Mehdi and Jitsev, Jenia and Maldonado Quinto, Daniel},
+    year = {2024},
+    issn = {0038-092X},
+    doi = {10.1016/j.solener.2024.112894},
+}
+
+@article{Lewen:2025,
+    publisher = {Elsevier},
+    title = {Inverse Deep Learning Raytracing for heliostat surface prediction},
+    author = {Lewen, Jan and Parmann, Max and Maldonado Quinto, Daniel and Jitsev, Jenia and Cherti, Mehdi and Pitz-Paal, Robert},
+    journal = {Solar Energy},
+    year = {2025},
+    month = {M{\"a}rz},
+    doi = {10.1016/j.solener.2025.113312},
+    keywords = {Inverse Deep Learning Raytracing, Deep Learning, Heliostat Surface Errors, Concentrating Solar Power},
+    issn = {0038-092X}
+}
+
+@article{Sattler:2020,
+    title = {Review of heliostat calibration and tracking control methods},
+    journal = {Solar Energy},
+    volume = {207},
+    pages = {110-132},
+    year = {2020},
+    issn = {0038-092X},
+    doi = {https://doi.org/10.1016/j.solener.2020.06.030},
+    author = {Johannes Christoph Sattler and Marc Röger and Peter Schwarzbözl and Reiner Buck and Ansgar Macke and Christian Raeder and Joachim Göttsche},
+}
+
+@article{Ulmer:2011,
+    title = {Automated high resolution measurement of heliostat slope errors},
+    journal = {Solar Energy},
+    volume = {85},
+    number = {4},
+    pages = {681-687},
+    year = {2011},
+    note = {SolarPACES 2009},
+    issn = {0038-092X},
+    doi = {https://doi.org/10.1016/j.solener.2010.01.010},
+    author = {Steffen Ulmer and Tobias März and Christoph Prahl and Wolfgang Reinalter and Boris Belhomme},
+}
+
+@book{Edenhofer:2011,
+    title={Renewable energy sources and climate change mitigation: Special report of the intergovernmental panel on climate change},
+    author={Edenhofer, Ottmar and Pichs-Madruga, Ram{\'o}n and Sokona, Youba and Seyboth, Kristin and Kadner, Susanne and Zwickel, Timm and Eickemeier, Patrick and Hansen, Gerrit and Schl{\"o}mer, Steffen and von Stechow, Christoph and others},
+    year={2011},
+    publisher={Cambridge University Press}
+}

joss_paper/paper.md

@@ -42,18 +42,30 @@ bibliography: paper.bib
 
 # Summary
 
-`artist`is a software package for concentrating solar power (CSP) plant digital twins. Solar tower power plants use an array of mirrors (heliostats), to reflect and concentrate sunlight onto a small area called the receiver. This process generates heat energy which is either used directly in industrial processes or to produce electricity. Efficient power plant operation is complex and differentiable digital twins can play an important role in enabling data-driven optimization and control. This Python package, `artist`, implements a fully differentiable digital twin for solar tower power plants, allowing for high-performance, memory-efficient optimization and parameter learning of the plant's components. At its core, the differentiable ray tracer simulates how light interacts with the three-dimensional scene, including environmental conditions, enabling gradient-based optimization from predicted flux distributions. By including differentiable models of all power plant components - including Non-Uniform Rational B-Splines (NURBS) surface models - `artist` can be used for highly accurate surface reconstruction, kinematic reconstruction, and aim point optimization. To ensure scalability, `artist` features native GPU acceleration, data-parallel processing, support for distributed computation, and is designed for portability across multiple hardware stacks.
+`artist`is a software package for the simulation and optimization of concentrating solar power (CSP) plant operation. Solar tower power plants use an array of mirrors (heliostats), to reflect and concentrate sunlight onto a small area called the receiver. The thermal power, represented through a flux distributions in the digital twin, can be used directly as high-temperature heat in industrial processes or it can be converted into climate-neutral electricity. This Python package, `artist`, implements a fully differentiable digital twin for solar tower power plants, providing tools for data-driven power plant component modeling and optimized power plant operation. The differentiable ray tracer in `artist` simulates how light interacts with the three-dimensional scene, including environmental conditions, enabling gradient-based optimizations with raytracing. In solar tower plants imperfect hardware is inevitable and leads to individually deformed fluxes for each heliostat. Surface deformations [@Ulmer:2011] and misalignments due to inaccurate kinematic [@Sattler:2020] components are main contributors for uncertainties. To improve efficiency in a solar tower power plant, each heliostats deformed and misaligned flux needs to aim at an individual point on the receiver for an optimal combined flux density distribution. The digital twin `artist` provides functionality to reconstruct the real-world heliostat surfaces and the heliostat kinematic. The simulation includes algorithms for alignment and raytracing to create flux density predictions. Based on this the heliostat aim points can be optimized. The main functionality of `artist` is shown in \autoref{fig:flowchart}.
 
+![Features of `artist`. 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 reconstruction, simulation and prediction. 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 for 3D surfaces. \label{fig:flowchart}](flowchart.png)
 
 # Statement of Need
 
-Concentrating solar power is a sustainable and renewable alternative to fossil fuels and nuclear energy, providing an environmentally friendly solution to meet the globally rising demand for energy [@CSPRoadMapNREL]. 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 mechanical imperfections, real-time control requirements and dynamic weather conditions [@Carballo:2025]. 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 [@Huang:2021]. While solar tower power plants may vary in their individual architectural details, their digital twins consistently rely on ray tracing. Conventional ray tracers [@Ahlbrink:2012], [@SolTrace], [@Tonatiuh:2018] 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 heliostat field layout optimization and solar tower design optimizations. The underlying concepts of `artist` are based on previous publications, which have demonstrated the potential of increasing solar tower power plant efficiency [@Pargmann:2024]. `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.
+In solar tower power plants, digital twins with precise simulation and reliable predictive capabilities are essential to realize fully autonomous power plant operation and a consequential reduction in costs [@Huang:2021]. While solar tower power plants may vary in their individual architectural details, their digital twins consistently rely on ray tracing. Conventional ray tracers for CSP [@Ahlbrink:2012; @SolTrace, @Tonatiuh:2018] achieve good results in simulating power plant behavior. However, they can only use ray tracing to make predictions based on the supplied data and the resulting model. From a machine learning perspective, these ray tracers are confined to forward computations. 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. Single differentiable digital twin tasks for CSP simulations have been addressed in related works, using inverse deep learning for heliostat surface modeling [@Lewen:2025] or generative neural networks for flux predictions [@Kuhl:2024]. In contrast to these black-box AI algorithms, `artist` maintains interpretability due to its physical models. In CSP technology, transparency in system behavior is critical for building trust in automated decision-making and software generated operation suggestions. The methodology in `artist` remains consistent across all optimization tasks, integrating modeling, prediction and optimization into one combined tool.
+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]. Therefore, anyone including people who do not have direct access to an operational power plant can use `artist`. We aim to strengthen community engagement and collaboration for further research advancements by developing `artist` as an easily accessible open-source software.
 
-# Features
+# State of the Field
 
-The main features of `artist` are shown in \autoref{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.
+On the one hand, commercially proven digital twin software solutions for solar tower power plants such as the raytracer STRAL [@Ahlbrink:2012] are non-differentiable and therefore lack the ability to reconstruct heliostat field models or to optimize power plant parameters. Instead they assume idealized hardware or provide tools for exact modeling which rely on infeasible measurements. These already commercially proven software solutions are typically proprietary and closed-source, limiting the possibilities for public contributions. On the other hand published differentiable proof-of-concept methods which improve digital twin simulation and enable optimization are partial, non-unified solutions which are not necessarily scalable. `artist` addresses these gaps by introducing a fully differentiable, open-source raytracer for CSP that integrates scalable, physics-informed AI methods into a single coherent framework. We build on the concept of differentiable raytracing for CSP by adding differentiable ray blocking. Furthermore we redefine existing proof-of-concept solutions under the unified approach of gradient descent optimization of physical models to ensure consistency across all digital twin tasks. As no comparable open-source alternative exists, we provide the basis for future contributions.
 
-![Features of `artist`, the AI-enhanced differentiable Ray Tracer for Irradiation Prediction in Solar Tower Digital Twins. \label{fig:flowchart}](flowchart.png)
+# Software Design
+
+`artist` is optimized in its design for computational efficiency by balancing memory consumption, execution time and simulation accuracy. To preserve its realtime capabilities and scalability in runtime when considering power plants with several thousand mirrors, `artist` features native GPU acceleration and supports distributed computation. On a single GPU `artist` parallelizes the computation of individual heliostats by leveraging the Structure of Arrays (SoA) format to store data contiguously in memory. Combined with the GPU’s Single-Instruction, Multiple-Thread (SMIT) processing model, the chosen data structure enables coalesced memory access, which minimizes memory overhead and maximizes GPU utilization. For distributed execution on multiple compute nodes `artist` uses a nested data-parallel approach that distributes groups of heliostats among the available compute resources. Additionally, each group can be parallelized through nested sub-processes. Heliostat within a single group share mathematically identical alignment calculations, which differ from the rest, so they must be separated for parallelized computation. In `artist`, parameter learning purely relies on gradient descent optimization combined with physics-informed models and constraints. This combination stabilizes the optimization of the underdetermined system and reduces the search space. It also enables fast convergence on fewer samples which in turn reduces latency and memory usage. Users can configure key simulation and optimization parameters to adjust the trade-off between memory usage, computational time and precision to their needs. The core algorithms in `artist` are implemented using fully differentiable methods building on PyTorch's automatic differentiation system to propagate gradients. Overall, `artist` adheres to the FAIR principles for research software (FAIR4RS) [@Barker:2022] and ensures portability across multiple hardware stacks. The modular architecture, built on abstraction and inheritance, enables its application across diverse solar tower power plant designs. Users can build on existing interfaces to incorporate specific design details of their power plants into the heliostat field models.
+
+# Research Impact Statement
+
+The underlying concepts of `artist` are based on previous publications, which demonstrate the potential of the differentiable ray tracing approach [@Pargmann:2024] and more generally show the potential of increasing solar tower power plant efficiency in order to provide an environmentally friendly solution to meet the globally rising demand for energy [@CSPRoadMapNREL; @Carballo:2020; @Edenhofer:2011].
+
+# AI Usage Disclosure
+
+No generative AI tools were used in the development of this software, the writing of this manuscript, or the preparation of supporting materials.
 
 # Acknowledgements