.. title:: Talk: ARTIST

.. only:: html

ARTIST, an open-source, PyTorch-based differentiable ray tracer for solar thermal power plants
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Speaker: Max Pargmann, DLR

Abstract
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Solar tower power plants concentrate sunlight from thousands of mirrors
(heliostats) onto a central receiver. While they could deliver high-temperature
heat for chemical processes and cheap, dispatchable energy, solar tower plants
have not taken off. The main barrier is complexity: individual component errors
undermine the high accuracy required, yet tight cost constraints rule out
dedicated metrology hardware. Their design and operation decompose into many
problems that have each traditionally demanded a bespoke method — heliostat
field layout, kinematic calibration of tracking errors, reconstruction of mirror
surfaces, or aim point optimization to shape the receiver flux.

We present ARTIST, an open-source, PyTorch-based differentiable ray tracer that
recasts these problems as gradient-based optimization through a single physical
forward model (Pargmann et al., 2024). The forward pass aligns each heliostat
via a rigid-body kinematic model, represents mirror facets as differentiable
NURBS surfaces, and traces sampled rays — including sun shape and environmental
conditions — to the receiver and calibration targets, accumulating a predicted
flux density distribution. Reverse-mode AD then propagates a loss back to
whichever parameters carry gradients: NURBS control points (surface
reconstruction), the kinematic deviation parameters per heliostat (calibration),
heliostat ground positions (field layout), or per-heliostat aim points (flux
homogenization to suppress receiver hotspots). The same primal serves both
inverse tasks, where the loss measures mismatch to recorded flux images, and
design tasks, where it encodes an efficiency or flux-quality objective.

The substance of this framework is in differentiating the tracer at scale:
handling discontinuities at ray–surface intersections, controlling variance in
gradients through Monte Carlo ray sampling, and keeping the reverse-mode tape
tractable across thousands of heliostats via a parallelized NURBS
implementation, GPU acceleration, and distributed computation. We validate
against real operational data from the Jülich solar tower, released openly
through the PAINT database (Phipps et al., 2026). The result is a unified
digital-twin environment where adding a new task means choosing new parameters
to differentiate, not building a new solver.

References
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* Pargmann, M., Ebert, J., Götz, M., Maldonado Quinto, D., Pitz-Paal, R., &
  Kesselheim, S. (2024). Automatic heliostat learning for in situ
  concentrating solar power plant metrology with differentiable ray tracing.
  Nature Communications, 15. https://doi.org/10.1038/s41467-024-51019-z

* Phipps, K., Kuhl, M., Weiel, M., Busch, M., Lewen, J., Blumenröhr, N.,
  Maldonado Quinto, D., Debus, C., Göhring, F., Streit, A., Pitz-Paal, R.,
  Götz, M., & Pargmann, M. (2026). PAINT: The First FAIR Database for
  Concentrating Solar Power Plants. Nature Energy.