tag:joss.theoj.org,2005:/papers/tagged/model%20reductionJournal of Open Source Software2022-12-12T11:00:17ZJournal of Open Source Softwarehttps://joss.theoj.orgtag:joss.theoj.org,2005:Paper/33062022-12-12T11:00:17Z2022-12-13T00:00:54ZFlexWing-ROM: A matlab framework for data-driven reduced-order modeling of flexible wingsacceptedv1.0.02022-01-27 23:12:42 UTC802022-12-12 11:00:17 UTC720224211UrbanFaselDepartment of Aeronautics, Imperial College, London, United KingdomNicolaFonziDepartment of Aerospace Science and Technology, Politecnico di Milano, Milan, ItalyAndreaIannelliDepartment of Information Technology and Electrical Engineering, ETH Zurich, Zurich, SwitzerlandStevenL.BruntonDepartment of Mechanical Engineering, University of Washington, Seattle, United States of America10.21105/joss.04211https://doi.org/10.5281/zenodo.7419465MATLAB, GLSL, C++https://joss.theoj.org/papers/10.21105/joss.04211.pdfMatlab, data-driven modeling, aeroelasticity, morphing wings, dynamic mode decomposition with controltag:joss.theoj.org,2005:Paper/25722022-11-08T04:00:51Z2022-11-09T00:00:44ZPERFORM: A Python package for developing reduced-order models for reacting fluid flowsacceptedv0.12021-04-13 21:07:29 UTC792022-11-08 04:00:51 UTC720223428ChristopherR.WentlandDepartment of Aerospace Engineering, University of Michigan0000-0002-8500-569XKarthikDuraisamyDepartment of Aerospace Engineering, University of Michigan10.21105/joss.03428https://doi.org/10.5281/zenodo.7302346Pythonhttps://joss.theoj.org/papers/10.21105/joss.03428.pdfcombustion, reduced-order modelstag:joss.theoj.org,2005:Paper/31142021-12-13T17:08:35Z2021-12-21T10:36:39ZflowTorch - a Python library for analysis and reduced-order modeling of fluid flowsacceptedv1.02021-10-18 13:47:35 UTC682021-12-13 17:08:35 UTC620213860AndreWeinerTechnische Universität Braunschweig, Institute of Fluid Mechanics, Flow Modeling and Control Group0000-0001-5617-1560RichardSemaanTechnische Universität Braunschweig, Institute of Fluid Mechanics, Flow Modeling and Control Group0000-0002-3219-054510.21105/joss.03860https://doi.org/10.5281/zenodo.5770244Python, C++https://joss.theoj.org/papers/10.21105/joss.03860.pdfPyTorch, fluid flows, reduced-order modeling, modal analysistag:joss.theoj.org,2005:Paper/20612021-06-15T12:41:58Z2021-06-16T00:00:46ZOpenSCM Two Layer Model: A Python implementation of the two-layer climate modelacceptedv0.1.22020-10-07 01:33:19 UTC622021-06-15 12:41:58 UTC620212766ZebedeeNichollsAustralian-German Climate & Energy College, The University of Melbourne, Parkville, Victoria, Australia, School of Geography, Earth and Atmosphere Sciences, The University of Melbourne, Parkville, Victoria, Australia, Climate Resource, Northcote, Victoria, Australia0000-0002-4767-2723JaredLewisAustralian-German Climate & Energy College, The University of Melbourne, Parkville, Victoria, Australia, School of Geography, Earth and Atmosphere Sciences, The University of Melbourne, Parkville, Victoria, Australia, Climate Resource, Northcote, Victoria, Australia0000-0002-8155-892410.21105/joss.02766https://doi.org/10.5281/zenodo.4950772Pythonhttps://joss.theoj.org/papers/10.21105/joss.02766.pdfclimate science, temperature projections, simple climate model, energy balance, reduced complexity climate modeltag:joss.theoj.org,2005:Paper/19282020-12-24T09:13:28Z2021-02-15T11:30:11Zqgs: A flexible Python framework of reduced-order multiscale climate modelsacceptedv0.2.02020-08-05 10:23:11 UTC562020-12-24 09:13:28 UTC520202597JonathanDemaeyerRoyal Meteorological Institute of Belgium, Avenue Circulaire, 3, 1180 Brussels, Belgium0000-0002-5098-404XLesleyDe CruzRoyal Meteorological Institute of Belgium, Avenue Circulaire, 3, 1180 Brussels, Belgium0000-0003-4458-8953StéphaneVannitsemRoyal Meteorological Institute of Belgium, Avenue Circulaire, 3, 1180 Brussels, Belgium0000-0002-1734-104210.21105/joss.02597https://doi.org/10.5281/zenodo.4368844Python, Jupyter Notebookhttps://joss.theoj.org/papers/10.21105/joss.02597.pdfNumba, Idealized atmospheric model, Coupled model, Mid-latitude climate variabilitytag:joss.theoj.org,2005:Paper/12752019-12-06T14:47:37Z2021-02-15T11:31:33ZPyUoI: The Union of Intersections Framework in Pythonaccepted1.0.02019-10-04 17:50:39 UTC442019-12-06 14:47:37 UTC420191799PratikS.SachdevaRedwood Center for Theoretical Neuroscience, University of California, Berkeley, Berkeley, California, USA, Department of Physics, University of California, Berkeley, Berkeley, California, USA, Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA0000-0002-6809-2437JesseA.LivezeyRedwood Center for Theoretical Neuroscience, University of California, Berkeley, Berkeley, California, USA, Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA0000-0003-0494-8758AndrewJ.TrittComputational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA0000-0002-1617-449XKristoferE.BouchardRedwood Center for Theoretical Neuroscience, University of California, Berkeley, Berkeley, California, USA, Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA, Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA, Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, USA0000-0002-1974-460310.21105/joss.01799https://doi.org/10.5281/zenodo.3563147C, Pythonhttps://joss.theoj.org/papers/10.21105/joss.01799.pdfgeneralized linear models, dimensionality reduction, sparsity, interpretabilitytag:joss.theoj.org,2005:Paper/10762019-09-08T15:43:44Z2021-02-15T11:32:03ZpyMARS: automatically reducing chemical kinetic models in Pythonacceptedv1.0.02019-06-24 20:47:30 UTC412019-09-08 15:43:44 UTC420191543PhillipO.MestasSchool of Mechanical, Industrial, and Manufacturing Engineering, Oregon State University, Corvallis, OR USA 973310000-0003-4379-3592ParkerClaytonSchool of Mechanical, Industrial, and Manufacturing Engineering, Oregon State University, Corvallis, OR USA 97331KyleE.NiemeyerSchool of Mechanical, Industrial, and Manufacturing Engineering, Oregon State University, Corvallis, OR USA 973310000-0003-4425-709710.21105/joss.01543https://doi.org/10.5281/zenodo.3401549Pythonhttps://joss.theoj.org/papers/10.21105/joss.01543.pdfchemical kinetics, model reductiontag:joss.theoj.org,2005:Paper/4322018-04-11T15:01:49Z2021-02-15T11:33:33ZEZyRB: Easy Reduced Basis methodacceptedv0.2.02018-03-10 10:05:30 UTC242018-04-11 15:01:49 UTC32018661NicolaDemoInternation School of Advanced Studies, SISSA, Trieste, Italy0000-0003-3107-9738MarcoTezzeleInternation School of Advanced Studies, SISSA, Trieste, Italy0000-0001-9747-6328GianluigiRozzaInternation School of Advanced Studies, SISSA, Trieste, Italy0000-0002-0810-881210.21105/joss.00661https://doi.org/10.5281/zenodo.1216303Python, Jupyter Notebookhttps://joss.theoj.org/papers/10.21105/joss.00661.pdfModel Order Reduction, Proper Orthogonal Decomposition, POD Interpolationtag:joss.theoj.org,2005:Paper/3782018-03-12T16:50:17Z2021-02-15T11:33:38ZThe vtreat R package: a statistically sound data processor for predictive modelingaccepted1.0.22018-02-09 23:19:43 UTC232018-03-12 16:50:17 UTC32018584JohnMountWin-Vector, LLC0000-0002-3696-2012NinaZumelWin-Vector, LLC0000-0001-8831-019010.21105/joss.00584https://doi.org/10.5281/zenodo.1196479Rhttps://joss.theoj.org/papers/10.21105/joss.00584.pdfdata science, predictive modeling, classification, regression, data preparation, significance, dimensionality reduction, reproducible research, cross-validationtag:joss.theoj.org,2005:Paper/592016-09-29T00:00:00Z2021-02-15T11:34:26ZPython Active-subspaces Utility Libraryacceptedv 0.12016-09-19 20:33:28 UTC52016-09-29 00:00:00 UTC1201679PaulConstantineColorado School of Mines, Golden, CO0000-0003-3726-6307RyanHowardColorado School of Mines, Golden, COAndrewGlawsColorado School of Mines, Golden, COZacharyGreyColorado School of Mines, Golden, COPaulDiazUniversity of Colorado Boulder, Boulder, COLeslieFletcherNone10.21105/joss.00079https://doi.org/10.5281/zenodo.158941Python, Jupyter Notebookhttps://joss.theoj.org/papers/10.21105/joss.00079.pdfpython, active subspaces, dimension reduction, uncertainty quantification, sensitivity analysis, surrogate modeling