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 # Stochastic Optimization under high-dimensional Uncertainty in Python
 
-**S**tochastic **O**ptimization under high-dimensional **U**ncertainty in **Py**thon—**SOUPy**, implements scalable algorithms for the optimization of large-scale complex systems governed by partial differential equations (PDEs) under high-dimensional uncertainty. The library features various risk measures (such as mean, variance, and superquantile/condition-value-at-risk), probability/chance constraints, and optimization/state constraints. SOUPy enables efficient PDE-constrained optimization under uncertainty through parallel computation of the risk measures and their derivatives (gradients and Hessians). The library also provides built-in parallel implementations of optimization algorithms (e.g. BFGS, Inexact Newton CG), as well as an interface to the `scipy.optimize` module in [SciPy](https://scipy.org/). Besides the benchmark/tutorial examples in the examples folder, SOUPy has been used to solve large-scale and high-dimensional stochastic optimization problems including [optimal control of turbulence flow](https://www.sciencedirect.com/science/article/pii/S0021999119301056), optimal design of [acoustic metamaterials](https://www.sciencedirect.com/science/article/pii/S0021999121000061), [self-assembly nanomaterials](https://www.sciencedirect.com/science/article/pii/S0021999123001961), and [photonic nanojets](https://arxiv.org/abs/2209.02454), and [optimal management of groundwater extraction](https://epubs.siam.org/doi/abs/10.1137/20M1381381), etc.
+**S**tochastic **O**ptimization under high-dimensional **U**ncertainty in **Py**thon—**SOUPy**, implements scalable algorithms for the optimization of large-scale complex systems governed by partial differential equations (PDEs) under high-dimensional uncertainty. The library features various risk measures (such as mean, variance, and superquantile/condition-value-at-risk), probability/chance constraints, and optimization/state constraints. SOUPy enables efficient PDE-constrained optimization under uncertainty through parallel computation of the risk measures and their derivatives (gradients and Hessians). The library also provides built-in parallel implementations of optimization algorithms (e.g. BFGS, Inexact Newton CG), as well as an interface to the `scipy.optimize` module in [SciPy](https://scipy.org/). Besides the benchmark/tutorial examples in the examples folder, SOUPy has been used to solve large-scale and high-dimensional stochastic optimization problems including [optimal control of turbulence flow](https://www.sciencedirect.com/science/article/pii/S0021999119301056), optimal design of [acoustic metamaterials](https://www.sciencedirect.com/science/article/pii/S0021999121000061) and [self-assembly nanomaterials](https://www.sciencedirect.com/science/article/pii/S0021999123001961),  and [optimal management of groundwater extraction](https://epubs.siam.org/doi/abs/10.1137/20M1381381), etc.
 
 SOUPy is built on the open-source [hIPPYlib library](https://hippylib.github.io/), which provides adjoint-based methods for deterministic and Bayesian inverse problems governed by PDEs, and makes use of [FEniCS](https://fenicsproject.org/) for the high-level formulation, discretization, and solution of PDEs.