tag:joss.theoj.org,2005:/papers/tagged/neural%20networkJournal of Open Source Software2024-03-06T17:32:25ZJournal of Open Source Softwarehttps://joss.theoj.orgtag:joss.theoj.org,2005:Paper/48582024-03-06T17:32:25Z2024-03-07T00:00:24Zcalorine: A Python package for constructing and sampling neuroevolution potential modelsacceptedv2.02023-10-21 15:02:27 UTC952024-03-06 17:32:25 UTC920246264EricLindgrenDepartment of Physics, Chalmers University of Technology, Gothenburg 412 96, Sweden0000-0002-8549-6839MagnusRahmDepartment of Physics, Chalmers University of Technology, Gothenburg 412 96, Sweden0000-0002-6777-0371ErikFranssonDepartment of Physics, Chalmers University of Technology, Gothenburg 412 96, Sweden0000-0001-5262-3339FredrikErikssonDepartment of Physics, Chalmers University of Technology, Gothenburg 412 96, Sweden0000-0002-7945-5483NicklasÖsterbackaDepartment of Physics, Chalmers University of Technology, Gothenburg 412 96, Sweden0000-0002-6043-4607ZheyongFanCollege of Physical Science and Technology, Bohai University, Jinzhou 121013, P. R. China0000-0002-2253-8210PaulErhartDepartment of Physics, Chalmers University of Technology, Gothenburg 412 96, Sweden0000-0002-2516-606110.21105/joss.06264https://doi.org/10.5281/zenodo.10723374Python, C++, Jupyter Notebookhttps://joss.theoj.org/papers/10.21105/joss.06264.pdfcondensed matter, machine learning, interatomic potentials, force fields, molecular dynamics, neuroevolution, neural networktag:joss.theoj.org,2005:Paper/48132024-02-27T21:24:38Z2024-03-22T14:09:24ZDeepRank2: Mining 3D Protein Structures with Geometric Deep Learningacceptedv2.1.02023-09-22 15:36:51 UTC942024-02-27 21:24:38 UTC920245983GiuliaCrocioniNetherlands eScience Center, Amsterdam, The Netherlands0000-0002-0823-0121DaniL.BodorNetherlands eScience Center, Amsterdam, The Netherlands0000-0003-2109-2349CoosBaakmanThe Department of Medical BioSciences, Radboud University Medical Center, Nijmegen, The Netherlands0000-0003-4317-1566FarzanehM.PariziThe Department of Medical BioSciences, Radboud University Medical Center, Nijmegen, The Netherlands0000-0003-4230-7492Daniel-T.RademakerThe Department of Medical BioSciences, Radboud University Medical Center, Nijmegen, The Netherlands0000-0003-1959-1317GayatriRamakrishnanThe Department of Medical BioSciences, Radboud University Medical Center, Nijmegen, The Netherlands0000-0001-8203-2783SvenA.van der BurgNetherlands eScience Center, Amsterdam, The Netherlands0000-0003-1250-6968DarioF.MarzellaThe Department of Medical BioSciences, Radboud University Medical Center, Nijmegen, The Netherlands0000-0002-0043-3055JoãoM.c.TeixeiraIndependent Researcher0000-0002-9113-0622LiC.XueThe Department of Medical BioSciences, Radboud University Medical Center, Nijmegen, The Netherlands0000-0002-2613-538X10.21105/joss.05983https://doi.org/10.5281/zenodo.10566809Python, Jupyter Notebookhttps://joss.theoj.org/papers/10.21105/joss.05983.pdfPyTorch, structural biology, geometric deep learning, 3D protein structures, protein-protein interfaces, single-residue variants, graph neural networks, convolutional neural networks, DeepRanktag:joss.theoj.org,2005:Paper/45532024-02-25T16:09:51Z2024-02-26T00:00:51ZPyKoopman: A Python Package for Data-Driven Approximation of the Koopman Operatoracceptedv1.0.32023-06-20 18:07:16 UTC942024-02-25 16:09:51 UTC920245881ShaowuPanDepartment of Applied Mathematics, University of Washington, Seattle, WA 98195, United States, Department of Mechanical, Aerospace, and Nuclear Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, United States0000-0002-2462-362XEurikaKaiserDepartment of Mechanical Engineering, University of Washington, Seattle, WA 98195, United States0000-0001-6049-0812BrianM.de SilvaDepartment of Applied Mathematics, University of Washington, Seattle, WA 98195, United States0000-0003-0944-900XJ.NathanKutzDepartment of Applied Mathematics, University of Washington, Seattle, WA 98195, United States0000-0002-6004-2275StevenL.BruntonDepartment of Mechanical Engineering, University of Washington, Seattle, WA 98195, United States0000-0002-6565-511810.21105/joss.05881https://doi.org/10.5281/zenodo.10685233Pythonhttps://joss.theoj.org/papers/10.21105/joss.05881.pdfdynamical systems, Koopman operator, system identification, machine learning, neural networkstag:joss.theoj.org,2005:Paper/45702023-09-22T03:40:40Z2023-09-25T08:21:56ZBayesFlow: Amortized Bayesian Workflows With Neural Networksacceptedv1.1.12023-06-27 14:58:24 UTC892023-09-22 03:40:40 UTC820235702StefanT.RadevCluster of Excellence STRUCTURES, Heidelberg University, Germany0000-0002-6702-9559MarvinSchmittCluster of Excellence SimTech, University of Stuttgart, Germany0000-0003-1293-820XLukasSchumacherInstitute for Psychology, Heidelberg University, Germany0000-0003-1512-8288LasseElsemüllerInstitute for Psychology, Heidelberg University, Germany0000-0003-0368-720XValentinPratzVisual Learning Lab, Heidelberg University, Germany0000-0001-8371-3417YannikSchälteLife and Medical Sciences Institute, University of Bonn, Germany0000-0003-1293-820XUllrichKötheVisual Learning Lab, Heidelberg University, Germany0000-0001-6036-1287Paul-ChristianBürknerCluster of Excellence SimTech, University of Stuttgart, Germany, Department of Statistics, TU Dortmund University, Germany0000-0001-5765-899510.21105/joss.05702https://doi.org/10.5281/zenodo.8346393Pythonhttps://joss.theoj.org/papers/10.21105/joss.05702.pdfsimulation-based inference, likelihood-free inference, Bayesian inference, amortized Bayesian inferencetag:joss.theoj.org,2005:Paper/42602023-09-20T00:32:03Z2023-09-21T00:01:17ZSpikeometric: Linear Non-Linear Cascade Spiking Neural Networks with Pytorch Geometricacceptedv1.0.02023-02-23 16:23:51 UTC892023-09-20 00:32:03 UTC820235451JakobL.SønstebøDepartment of Numerical Analysis and Scientific Computing, Simula Research Laboratory, Oslo, Norway0009-0009-0584-9293HermanBrunborgDepartment of Physics, University of Oslo, Oslo, NorwayMikkelElleLepperødDepartment of Numerical Analysis and Scientific Computing, Simula Research Laboratory, Oslo, Norway, Department of Physics, University of Oslo, Oslo, Norway0000-0002-4262-554910.21105/joss.05451https://doi.org/10.5281/zenodo.8358903Pythonhttps://joss.theoj.org/papers/10.21105/joss.05451.pdfpython, computational neuroscience, machine learning, spiking neural networks, generalized linear models, linear non-linear poisson modelstag:joss.theoj.org,2005:Paper/40032023-08-25T03:11:09Z2023-08-26T00:01:24ZGNS: A generalizable Graph Neural Network-based simulator for particulate and fluid modelingacceptedv1.0.12022-11-13 22:08:21 UTC882023-08-25 03:11:09 UTC820235025KrishnaKumarAssistant Professor, University of Texas at Austin, Texas, USA0000-0003-2144-5562JosephVantasselAssistant Professor, Virginia Tech, Virginia, USA, Texas Advanced Computing Center, University of Texas at Austin, Texas, USA0000-0002-1601-335410.21105/joss.05025https://doi.org/10.5281/zenodo.8249813Python, Jupyter Notebookhttps://joss.theoj.org/papers/10.21105/joss.05025.pdfmachine learning, simulationtag:joss.theoj.org,2005:Paper/38282023-07-26T13:30:49Z2023-07-27T00:01:46ZAmpTorch: A Python package for scalable fingerprint-based neural network training on multi-element systems with integrated uncertainty quantificationacceptedv0.12022-08-22 16:32:43 UTC872023-07-26 13:30:49 UTC820235035MuhammedShuaibiDepartment of Chemical Engineering, Carnegie Mellon University, United StatesYugeHuDepartment of Chemical and Biomolecular Engineering, Georgia Institute of Technology, United States0000-0003-3648-7749XiangyunLeiDepartment of Chemical and Biomolecular Engineering, Georgia Institute of Technology, United StatesBenjaminM.ComerDepartment of Chemical and Biomolecular Engineering, Georgia Institute of Technology, United StatesMattAdamsDepartment of Chemical Engineering, Carnegie Mellon University, United StatesJacobParasSchool of Physics and School of Computer Science, Georgia Institute of Technology, United StatesRuiQiChenDepartment of Chemical and Biomolecular Engineering, Georgia Institute of Technology, United StatesEricMusaDepartment of Chemical Engineering, University of Michigan, United StatesJosephMusielewiczDepartment of Chemical Engineering, Carnegie Mellon University, United StatesAndrewA.PetersonSchool of Engineering, Brown University, United States0000-0003-2855-9482AndrewJ.MedfordDepartment of Chemical and Biomolecular Engineering, Georgia Institute of Technology, United States0000-0001-8311-9581ZacharyUlissiDepartment of Chemical Engineering, Carnegie Mellon University, United States0000-0002-9401-491810.21105/joss.05035https://doi.org/10.5281/zenodo.8151492Python, C++, C, G-code, GAPhttps://joss.theoj.org/papers/10.21105/joss.05035.pdfmachine learning interatomic potentials, neural networks, molecular dynamicstag:joss.theoj.org,2005:Paper/43582023-07-19T17:41:02Z2023-07-20T00:01:41ZPhysics-Informed Neural networks for Advanced modelingaccepted0.0.22023-03-21 14:04:01 UTC872023-07-19 17:41:02 UTC820235352DarioCosciaSISSA, International School of Advanced Studies, Via Bonomea 265, Trieste, Italy0000-0001-8833-6833AnnaIvagnesSISSA, International School of Advanced Studies, Via Bonomea 265, Trieste, Italy0000-0002-2369-4493NicolaDemoSISSA, International School of Advanced Studies, Via Bonomea 265, Trieste, Italy0000-0003-3107-9738GianluigiRozzaSISSA, International School of Advanced Studies, Via Bonomea 265, Trieste, Italy0000-0002-0810-881210.21105/joss.05352https://doi.org/10.5281/zenodo.8163732Pythonhttps://joss.theoj.org/papers/10.21105/joss.05352.pdfpython, deep learning, physics-informed neural networks, scientific machine learning, differential equations.tag:joss.theoj.org,2005:Paper/39652023-07-05T12:56:44Z2023-07-06T00:01:11ZDeBEIR: A Python Package for Dense Bi-Encoder Information Retrievalacceptedv0.0.12022-10-17 06:11:59 UTC872023-07-05 12:56:44 UTC820235017VincentNguyenAustralian National University, School of Computing, Commonwealth Scientific and Industrial Research Organisation, Data610000-0003-1787-8090SarvnazKarimiCommonwealth Scientific and Industrial Research Organisation, Data610000-0002-4927-3937ZhenchangXingAustralian National University, School of Computing, Commonwealth Scientific and Industrial Research Organisation, Data610000-0001-7663-142110.21105/joss.05017https://doi.org/10.5281/zenodo.8103783Python, Jupyter Notebookhttps://joss.theoj.org/papers/10.21105/joss.05017.pdfinformation retrieval, dense retrieval, bi-encoder, transformers, pytorch, python, deep learning, neural networks, machine learning, natural language processingtag:joss.theoj.org,2005:Paper/39442023-05-12T12:14:29Z2023-05-13T00:01:21ZGraphNeT: Graph neural networks for neutrino telescope event reconstructionacceptedv0.2.22022-10-06 11:55:25 UTC852023-05-12 12:14:29 UTC820234971AndreasSøgaardNiels Bohr Institute, University of Copenhagen, Denmark0000-0002-0823-056XRasmusF.ØrsøeNiels Bohr Institute, University of Copenhagen, Denmark, Technical University of Munich, Germany0000-0001-8890-4124MortenHolmNiels Bohr Institute, University of Copenhagen, Denmark0000-0003-1383-2810LeonBozianuNiels Bohr Institute, University of Copenhagen, Denmark0000-0002-1243-9980AskeRostedChiba University, Japan0000-0003-2410-400XTroelsC.PetersenNiels Bohr Institute, University of Copenhagen, Denmark0000-0003-0221-3037KaareEndrupIversenNiels Bohr Institute, University of Copenhagen, Denmark0000-0001-6533-4085AndreasHermansenNiels Bohr Institute, University of Copenhagen, Denmark0009-0006-1162-9770TimGuggenmosTechnical University of Munich, GermanyPeterAndresenNiels Bohr Institute, University of Copenhagen, Denmark0009-0008-5759-0490MartinHaMinhTechnical University of Munich, Germany0000-0001-7776-4875LudwigNesteTechnical University of Dortmund, Germany0000-0002-4829-3469MoustHolmesNiels Bohr Institute, University of Copenhagen, Denmark0009-0000-8530-7041AxelPonténUppsala University, Sweden0009-0008-2463-2930KaylaLeonardDeHoltonPennsylvania State University, USA0000-0002-8795-0601PhilippEllerTechnical University of Munich, Germany0000-0001-6354-520910.21105/joss.04971https://doi.org/10.5281/zenodo.7928487Pythonhttps://joss.theoj.org/papers/10.21105/joss.04971.pdfmachine learning, deep learning, neural networks, graph neural networks, astrophysics, particle physics, neutrinos