fix typos

Author: kalebphipps

joss_paper/paper.md

@@ -42,7 +42,7 @@ 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, a 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 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.
 
 
 # Statement of Need