From 42ee2d18f675a5386a3cfec6198a15434c8e2d2b Mon Sep 17 00:00:00 2001 From: "SciML-UQ@Georgia Tech" Date: Fri, 11 Aug 2023 08:57:41 -0400 Subject: [PATCH] minor update of README. --- README.md | 41 ++++++++++++++++++++++++++++++++--------- 1 file changed, 32 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index 97c3ee1..bc703a1 100644 --- a/README.md +++ b/README.md @@ -4,23 +4,46 @@ # Stochastic Optimization under high-dimensional Uncertainty in Python -**S**tochastic **O**ptimization under high-dimensional **U**ncertainty in **Py**thon—**SOUPy**, -is implements scalable algorithms for the optimization of risk measures such as mean, variance, and superquantile/condition-value-at-risk, subject to PDE constraints. -SOUPy enables efficient PDE-constrained optimization under uncertainty through parallel computation of risk measures and their derivatives (gradients and Hessians). -The library provides built-in implementations of large-scale optimization algorithms (e.g. BFGS, Inexact Newton CG), as well as an interface to the `scipy.optimize` module in [SciPy](https://scipy.org/). +**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. 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. SOUPy is in active development to incorporate advanced approximation algorithms and capabilities, including: + - Taylor expansion-based approximations for risk measure evaluation - High-dimensional quadrature methods such as sparse grids and quasi Monte Carlo -- Decomposition of uncertain parameter spaces by mixture models -- Multi-fidelity methods and control variates -- Interfaces with Bayesian inverse problems +- Decomposition of high-dimensional uncertain parameter spaces by mixture models +- Multi-fidelity methods and control variates +- Interfaces with Bayesian inverse problems See the [SOUPy documentation](https://hippylibsoupy.readthedocs.io/en/latest/) for more information. +Please consider to cite the following papers for using SOUPy + +``` +@article{chen2019taylor, + title={Taylor approximation and variance reduction for PDE-constrained optimal control under uncertainty}, + author={Chen, Peng and Villa, Umberto and Ghattas, Omar}, + journal={Journal of Computational Physics}, + volume={385}, + pages={163--186}, + year={2019}, + publisher={Elsevier} +} +``` + + +``` +@article{luo2023efficient, + title={Efficient PDE-Constrained optimization under high-dimensional uncertainty using derivative-informed neural operators}, + author={Luo, Dingcheng and O'Leary-Roseberry, Thomas and Chen, Peng and Ghattas, Omar}, + journal={arXiv preprint arXiv:2305.20053}, + year={2023} +} +``` + + ### Acknowledgements -This project is partially supported by NSF grants [#2012453](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2012453&HistoricalAwards=false) -and [#2245674](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2245674). + +This project is partially supported by NSF grants [#2012453](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2012453&HistoricalAwards=false) and [#2245674](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2245674).