{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T23:31:19Z","timestamp":1768001479197,"version":"3.49.0"},"reference-count":18,"publisher":"The Open Journal","issue":"99","license":[{"start":{"date-parts":[[2024,7,28]],"date-time":"2024-07-28T00:00:00Z","timestamp":1722124800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2024,7,28]],"date-time":"2024-07-28T00:00:00Z","timestamp":1722124800000},"content-version":"am","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2024,7,28]],"date-time":"2024-07-28T00:00:00Z","timestamp":1722124800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JOSS"],"published-print":{"date-parts":[[2024,7,28]]},"DOI":"10.21105\/joss.06101","type":"journal-article","created":{"date-parts":[[2024,7,28]],"date-time":"2024-07-28T13:24:05Z","timestamp":1722173045000},"page":"6101","source":"Crossref","is-referenced-by-count":2,"title":["SOUPy: Stochastic PDE-constrained optimization under\nhigh-dimensional uncertainty in Python"],"prefix":"10.21105","volume":"9","author":[{"given":"Dingcheng","family":"Luo","sequence":"first","affiliation":[]},{"given":"Peng","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Thomas","family":"O\u2019Leary-Roseberry","sequence":"additional","affiliation":[]},{"given":"Umberto","family":"Villa","sequence":"additional","affiliation":[]},{"given":"Omar","family":"Ghattas","sequence":"additional","affiliation":[]}],"member":"8722","reference":[{"key":"ChenVillaGhattas19","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2019.01.047","article-title":"Taylor approximation and variance reduction\nfor PDE-constrained optimal control under uncertainty","volume":"385","author":"Chen","year":"2019","unstructured":"Chen, P., Villa, U., & Ghattas,\nO. (2019). Taylor approximation and variance reduction for\nPDE-constrained optimal control under uncertainty. Journal of\nComputational Physics, 385, 163\u2013186.\nhttps:\/\/doi.org\/10.1016\/j.jcp.2019.01.047","journal-title":"Journal of Computational\nPhysics"},{"key":"ChenHabermanGhattas21","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2021.110114","article-title":"Optimal design of acoustic metamaterial\ncloaks under uncertainty","volume":"431","author":"Chen","year":"2021","unstructured":"Chen, P., Haberman, M., &\nGhattas, O. (2021). Optimal design of acoustic metamaterial cloaks under\nuncertainty. Journal of Computational Physics, 431, 110114.\nhttps:\/\/doi.org\/10.1016\/j.jcp.2021.110114","journal-title":"Journal of Computational\nPhysics"},{"issue":"2","key":"VillaPetraGhattas21","doi-asserted-by":"publisher","DOI":"10.1145\/3428447","article-title":"HIPPYlib: An extensible software framework\nfor large-scale inverse problems governed by PDEs: Part I: Deterministic\ninversion and linearized Bayesian inference","volume":"47","author":"Villa","year":"2021","unstructured":"Villa, U., Petra, N., & Ghattas,\nO. (2021). HIPPYlib: An extensible software framework for large-scale\ninverse problems governed by PDEs: Part I: Deterministic inversion and\nlinearized Bayesian inference. ACM Transactions on Mathematical\nSoftware, 47(2). https:\/\/doi.org\/10.1145\/3428447","journal-title":"ACM Transactions on Mathematical\nSoftware","ISSN":"https:\/\/id.crossref.org\/issn\/0098-3500","issn-type":"print"},{"issue":"30","key":"VillaPetraGhattas18","doi-asserted-by":"publisher","DOI":"10.21105\/joss.00940","article-title":"hIPPYlib: an Extensible Software Framework\nfor Large-scale Deterministic and Bayesian Inverse\nProblems","volume":"3","author":"Villa","year":"2018","unstructured":"Villa, U., Petra, N., & Ghattas,\nO. (2018). hIPPYlib: an Extensible Software Framework for Large-scale\nDeterministic and Bayesian Inverse Problems. Journal of Open Source\nSoftware, 3(30).\nhttps:\/\/doi.org\/10.21105\/joss.00940","journal-title":"Journal of Open Source\nSoftware"},{"key":"LuoOLearyRoseberryChenEtAl23","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2305.20053","article-title":"Efficient PDE-constrained optimization under\nhigh-dimensional uncertainty using derivative-informed neural\noperators","author":"Luo","year":"2023","unstructured":"Luo, D., O\u2019Leary-Roseberry, T., Chen,\nP., & Ghattas, O. (2023). Efficient PDE-constrained optimization\nunder high-dimensional uncertainty using derivative-informed neural\noperators.\nhttps:\/\/doi.org\/10.48550\/arXiv.2305.20053"},{"key":"LoggMardalWells12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-23099-8","volume-title":"Automated solution of differential equations\nby the finite element method","volume":"84","year":"2012","unstructured":"Logg, A., Mardal, K.-A., & Wells,\nG. N. (Eds.). (2012). Automated solution of differential equations by\nthe finite element method (Vol. 84). Springer.\nhttps:\/\/doi.org\/10.1007\/978-3-642-23099-8"},{"key":"2020SciPy-NMeth","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-019-0686-2","article-title":"SciPy 1.0: Fundamental algorithms for\nscientific computing in Python","volume":"17","author":"Virtanen","year":"2020","unstructured":"Virtanen, P., Gommers, R., Oliphant,\nT. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson,\nP., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson,\nJ., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R.,\nLarson, E., \u2026 SciPy 1.0 Contributors. (2020). SciPy 1.0: Fundamental\nalgorithms for scientific computing in Python. Nature Methods, 17,\n261\u2013272.\nhttps:\/\/doi.org\/10.1038\/s41592-019-0686-2","journal-title":"Nature Methods"},{"key":"LiuNocedal89","doi-asserted-by":"publisher","DOI":"10.1007\/BF01589116","article-title":"On the limited memory BFGS methods for large\nscale optimization","volume":"45","author":"Liu","year":"1989","unstructured":"Liu, D. C., & Nocedal, J. (1989).\nOn the limited memory BFGS methods for large scale optimization.\nMathematical Programming, 45, 503\u2013528.\nhttps:\/\/doi.org\/10.1007\/BF01589116","journal-title":"Mathematical Programming"},{"key":"Steihaug83","doi-asserted-by":"publisher","DOI":"10.1007\/BF02591944","article-title":"Local and superlinear convergence for\ntruncated iterated projections methods","volume":"27","author":"Steihaug","year":"1983","unstructured":"Steihaug, T. (1983). Local and\nsuperlinear convergence for truncated iterated projections methods.\nMathematical Programming, 27, 176\u2013190.\nhttps:\/\/doi.org\/10.1007\/BF02591944","journal-title":"Mathematical Programming"},{"issue":"1","key":"EisenstatWalker96","doi-asserted-by":"publisher","DOI":"10.1137\/0917003","article-title":"Choosing the forcing terms in an inexact\nNewton method","volume":"17","author":"Eisenstat","year":"1996","unstructured":"Eisenstat, S. C., & Walker, H. F.\n(1996). Choosing the forcing terms in an inexact Newton method. SIAM\nJournal on Scientific Computing, 17(1), 16\u201332.\nhttps:\/\/doi.org\/10.1137\/0917003","journal-title":"SIAM Journal on Scientific\nComputing"},{"issue":"4","key":"ChenGhattas21","doi-asserted-by":"publisher","DOI":"10.1137\/20M1381381","article-title":"Taylor approximation for chance constrained\noptimization problems governed by partial differential equations with\nhigh-dimensional random parameters","volume":"9","author":"Chen","year":"2021","unstructured":"Chen, P., & Ghattas, O. (2021).\nTaylor approximation for chance constrained optimization problems\ngoverned by partial differential equations with high-dimensional random\nparameters. SIAM\/ASA Journal on Uncertainty Quantification, 9(4),\n1381\u20131410. https:\/\/doi.org\/10.1137\/20M1381381","journal-title":"SIAM\/ASA Journal on Uncertainty\nQuantification"},{"issue":"38","key":"MituschFunkeDokken2019","doi-asserted-by":"publisher","DOI":"10.21105\/joss.01292","article-title":"Dolfin-adjoint 2018.1: Automated adjoints for\nFEniCS and firedrake","volume":"4","author":"Mitusch","year":"2019","unstructured":"Mitusch, S. K., Funke, S. W., &\nDokken, J. S. (2019). Dolfin-adjoint 2018.1: Automated adjoints for\nFEniCS and firedrake. Journal of Open Source Software, 4(38), 1292.\nhttps:\/\/doi.org\/10.21105\/joss.01292","journal-title":"Journal of Open Source\nSoftware"},{"issue":"2","key":"AlnaesMartinLoggEtAl14","doi-asserted-by":"publisher","DOI":"10.1145\/2566630","article-title":"Unified form language: A domain-specific\nlanguage for weak formulations of partial differential\nequations","volume":"40","author":"Aln\u00e6s","year":"2014","unstructured":"Aln\u00e6s, M. S., Logg, A., \u00d8lgaard, K.\nB., Rognes, M. E., & Wells, G. N. (2014). Unified form language: A\ndomain-specific language for weak formulations of partial differential\nequations. ACM Transactions on Mathematical Software, 40(2).\nhttps:\/\/doi.org\/10.1145\/2566630","journal-title":"ACM Transactions on Mathematical\nSoftware","ISSN":"https:\/\/id.crossref.org\/issn\/0098-3500","issn-type":"print"},{"key":"trilinos-website","volume-title":"Trilinos Project website","author":"Trilinos Project Team","year":"2024","unstructured":"Trilinos Project Team. (2024).\nTrilinos Project website.\nhttps:\/\/web.archive.org\/web\/20240228185301\/https:\/\/trilinos.github.io\/rol.html"},{"key":"AlghamdiChenKaramehmedovic22","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2209.02454","article-title":"Optimal design of photonic nanojets under\nuncertainty","author":"Alghamdi","year":"2022","unstructured":"Alghamdi, A., Chen, P., &\nKaramehmedovi\u0107, M. (2022). Optimal design of photonic nanojets under\nuncertainty.\nhttps:\/\/doi.org\/10.48550\/arXiv.2209.02454"},{"key":"KouriShapiro18","isbn-type":"print","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4939-8636-1_2","article-title":"Optimization of PDEs with uncertain\ninputs","author":"Kouri","year":"2018","unstructured":"Kouri, D. P., & Shapiro, A.\n(2018). Optimization of PDEs with uncertain inputs. In H. Antil, D. P.\nKouri, M.-D. Lacasse, & D. Ridzal (Eds.), Frontiers in\nPDE-constrained optimization (pp. 41\u201381). Springer New York.\nhttps:\/\/doi.org\/10.1007\/978-1-4939-8636-1_2","ISBN":"https:\/\/id.crossref.org\/isbn\/9781493986361","journal-title":"Frontiers in PDE-constrained\noptimization"},{"key":"KouriRidzalWinckel17","article-title":"Rapid optimization library","author":"Kouri","year":"2017","unstructured":"Kouri, D., Ridzal, D., & Winckel,\nG. von. (2017). Rapid optimization library. Sandia National\nLaboratories.\nhttps:\/\/trilinos.github.io\/pdfs\/ROL.pdf"},{"issue":"3","key":"RockafellarUryasev00","doi-asserted-by":"publisher","DOI":"10.21314\/jor.2000.038","article-title":"Optimization of conditional\nvalue-at-risk","volume":"2","author":"Rockafellar","year":"2000","unstructured":"Rockafellar, R. T., & Uryasev, S.\n(2000). Optimization of conditional value-at-risk. The Journal of Risk,\n2(3), 21\u201341.\nhttps:\/\/doi.org\/10.21314\/jor.2000.038","journal-title":"The Journal of Risk"}],"container-title":["Journal of Open Source Software"],"original-title":[],"link":[{"URL":"https:\/\/joss.theoj.org\/papers\/10.21105\/joss.06101.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,7,28]],"date-time":"2024-07-28T13:24:11Z","timestamp":1722173051000},"score":1,"resource":{"primary":{"URL":"https:\/\/joss.theoj.org\/papers\/10.21105\/joss.06101"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,28]]},"references-count":18,"journal-issue":{"issue":"99","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["10.21105\/joss.06101"],"URL":"https:\/\/doi.org\/10.21105\/joss.06101","relation":{"has-review":[{"id-type":"uri","id":"https:\/\/github.com\/openjournals\/joss-reviews\/issues\/6101","asserted-by":"subject"}],"references":[{"id-type":"doi","id":"10.5281\/zenodo.12997883","asserted-by":"subject"}]},"ISSN":["2475-9066"],"issn-type":[{"value":"2475-9066","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,28]]}}}