tag:joss.theoj.org,2005:/papers/tagged/ontJournal of Open Source Software2023-11-21T16:27:28ZJournal of Open Source Softwarehttps://joss.theoj.orgtag:joss.theoj.org,2005:Paper/44572023-11-21T16:27:28Z2023-11-22T08:46:01ZQMCTorch: a PyTorch Implementation of Real-Space Quantum Monte Carlo Simulations of Molecular Systemsacceptedv0.3.02023-04-28 08:45:41 UTC912023-11-21 16:27:28 UTC820235472NicolasRenaudNetherlands eScience Center, Science Park 402, 1098 XH Amsterdam, The Netherlands0000-0001-9589-269410.21105/joss.05472https://doi.org/10.5281/zenodo.10122190Pythonhttps://joss.theoj.org/papers/10.21105/joss.05472.pdfDeep Learning, Quantum Chemistry, Monte Carlo, Molecular Systemstag:joss.theoj.org,2005:Paper/46082023-09-28T19:37:54Z2023-09-29T11:54:28ZBlackBIRDS: Black-Box Inference foR Differentiable Simulatorsacceptedv1.02023-07-17 13:26:44 UTC892023-09-28 19:37:54 UTC820235776ArnauQuera-BofarullDepartment of Computer Science, University of Oxford, UK, Institute for New Economic Thinking, University of Oxford, UK0000-0001-5055-9863JoelDyerDepartment of Computer Science, University of Oxford, UK, Institute for New Economic Thinking, University of Oxford, UK0000-0002-8304-8450AnisoaraCalinescuDepartment of Computer Science, University of Oxford, UK0000-0003-2082-734XJ.DoyneFarmerInstitute for New Economic Thinking, University of Oxford, UK, Mathematical Institute, University of Oxford, UK, Santa Fe Institute, USA0000-0001-7871-073XMichaelWooldridgeDepartment of Computer Science, University of Oxford, UK0000-0002-9329-841010.21105/joss.05776https://doi.org/10.5281/zenodo.8377044Pythonhttps://joss.theoj.org/papers/10.21105/joss.05776.pdfBayesian inference, differentiable simulators, variational inference, Markov chain Monte Carlotag:joss.theoj.org,2005:Paper/45632023-09-17T18:20:22Z2023-09-18T00:01:06ZSimulated Diffusion in Realistic Imaging Features of Tissue (Sim-DRIFT)acceptedv1.0.02023-06-25 16:14:13 UTC892023-09-17 18:20:22 UTC820235621JacobBlumWashington University in St. Louis, USA0000-0002-4156-4094KainenL.UttWashington University in St. Louis, USA0000-0002-8555-900010.21105/joss.05621https://doi.org/10.5281/zenodo.8351982Pythonhttps://joss.theoj.org/papers/10.21105/joss.05621.pdfDiffusion MRI, Diffusion Tensor Imaging, Biophysics, Monte-Carlo Simulation, CUDAtag:joss.theoj.org,2005:Paper/44502023-07-26T13:23:53Z2023-07-27T00:01:44ZPxMCMC: A Python package for proximal Markov Chain Monte Carloacceptedv0.1.52023-04-24 05:20:17 UTC872023-07-26 13:23:53 UTC820235582AugustinMarignierResearch School of Earth Sciences, Australian National University, Australia0000-0001-6778-139910.21105/joss.05582https://doi.org/10.5281/zenodo.8185139Pythonhttps://joss.theoj.org/papers/10.21105/joss.05582.pdfMCMC, imaging, geophysics, astrophysicstag:joss.theoj.org,2005:Paper/41452023-06-24T19:41:17Z2023-06-25T00:01:04ZFast Resampling and Monte Carlo Methods in Pythonaccepted1.11.0.dev0+1225.ca8d4812023-01-04 00:32:58 UTC862023-06-24 19:41:17 UTC820235092MattHaberlandCalifornia Polytechnic State University, San Luis Obispo, USA0000-0003-4806-360110.21105/joss.05092https://doi.org/10.5281/zenodo.8031631Starlark, Python, Meson, Chttps://joss.theoj.org/papers/10.21105/joss.05092.pdfSciPy, statistics, bootstrap, confidence intervals, permutation tests, monte carlo methods, hypothesis testingtag:joss.theoj.org,2005:Paper/42202023-04-23T06:46:52Z2023-04-24T00:02:35ZQuasi-Monte Carlo Methods in Pythonaccepted1.102023-02-17 17:53:47 UTC842023-04-23 06:46:52 UTC820235309PamphileT.RoyQuansight0000-0001-9816-1416ArtB.OwenStanford University0000-0001-5860-3945MaximilianBalandatMeta0000-0002-8214-8935MattHaberlandCalifornia Polytechnic State University, San Luis Obispo, USA0000-0003-4806-360110.21105/joss.05309https://doi.org/10.5281/zenodo.7849706Starlark, Python, Meson, Chttps://joss.theoj.org/papers/10.21105/joss.05309.pdfSciPy, statistics, Quasi-Monte Carlo methodstag:joss.theoj.org,2005:Paper/36142023-04-19T13:32:01Z2023-04-20T00:02:54ZNew developments in PySDM and PySDM-examples v2: collisional breakup, immersion freezing, dry aerosol initialization, and adaptive time-steppingacceptedv2.82022-05-19 01:14:11 UTC842023-04-19 13:32:01 UTC820234968EmilyK.de JongDepartment of Mechanical and Civil Engineering, California Institute of Technology, Pasadena, CA, United States of America0000-0002-5310-4554ClareE.SingerDepartment of Environmental Science and Engineering, California Institute of Technology, Pasadena, CA, United States of America0000-0002-1708-0997SajjadAzimiDepartment of Environmental Science and Engineering, California Institute of Technology, Pasadena, CA, United States of America0000-0002-6329-7775PiotrBartmanFaculty of Mathematics and Computer Science, Jagiellonian University, Kraków, Poland0000-0003-0265-6428OleksiiBulenokFaculty of Mathematics and Computer Science, Jagiellonian University, Kraków, Poland0000-0003-2272-8548KacperDerlatkaFaculty of Mathematics and Computer Science, Jagiellonian University, Kraków, Poland0000-0003-3137-1288IsabellaDulaDepartment of Environmental Science and Engineering, California Institute of Technology, Pasadena, CA, United States of AmericaAnnaJarugaDepartment of Environmental Science and Engineering, California Institute of Technology, Pasadena, CA, United States of America0000-0003-3194-6440J.BenMackayDepartment of Environmental Science and Engineering, California Institute of Technology, Pasadena, CA, United States of America, Scripps Institution of Oceanography, San Diego, CA, United States of America0000-0001-8677-3562RyanX.WardDepartment of Environmental Science and Engineering, California Institute of Technology, Pasadena, CA, United States of America0000-0003-2317-3310SylwesterArabasFaculty of Mathematics and Computer Science, Jagiellonian University, Kraków, Poland, Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, United States of America0000-0003-2361-008210.21105/joss.04968https://doi.org/10.5281/zenodo.7640495Pythonhttps://joss.theoj.org/papers/10.21105/joss.04968.pdfphysics-simulation, monte-carlo-simulation, atmospheric-modeling, particle-system, atmospheric-physicstag:joss.theoj.org,2005:Paper/37592023-02-15T13:28:09Z2023-02-16T00:00:48ZPoUnce: A framework for automatized uncertainty quantification simulations on high-performance clustersacceptedv1.0.02022-08-02 14:41:27 UTC822023-02-15 13:28:09 UTC820234683JakobDuerrwaechterInstitute of Aerodynamics and Gas Dynamics, University of Stuttgart, Germany0000-0001-8961-5340ThomasKuhnInstitute of Aerodynamics and Gas Dynamics, University of Stuttgart, GermanyFabianMeyerInstitute of Applied Analysis and Numerical Simulation, University of Stuttgart, GermanyAndreaBeckInstitute of Aerodynamics and Gas Dynamics, University of Stuttgart, Germany, The Laboratory of Fluid Dynamics and Technical Flows, Otto von Guericke University Magdeburg, GermanyClaus-DieterMunzInstitute of Aerodynamics and Gas Dynamics, University of Stuttgart, Germany10.21105/joss.04683https://doi.org/10.5281/zenodo.7600634Pythonhttps://joss.theoj.org/papers/10.21105/joss.04683.pdfUncertainty quantification, High performance computing, Mulitlevel Monte Carlo, Multifidelity Monte Carlotag:joss.theoj.org,2005:Paper/35972022-10-14T15:44:28Z2022-10-15T00:00:39ZRELSAD: A Python package for reliability assessment of modern distribution systemsacceptedv0.0.12022-05-11 16:20:37 UTC782022-10-14 15:44:28 UTC720224516StineFleischerMyhreDepartment of Electric Power Engineering, NTNU, Trondheim, Norway0000-0002-2283-1724OlavBjarteFossoDepartment of Electric Power Engineering, NTNU, Trondheim, Norway0000-0002-3460-5839PoulEinarHeegaardDepartment of Information Security and Communication Technology, NTNU, Trondheim, Norway0000-0003-0083-5860OddbjørnGjerdeSINTEF Energy Research, Trondheim, Norway0000-0002-7978-747X10.21105/joss.04516https://doi.org/10.5281/zenodo.7195034Pythonhttps://joss.theoj.org/papers/10.21105/joss.04516.pdfElectrical engineering, Monte Carlo, Reliability, Smart gridtag:joss.theoj.org,2005:Paper/35202022-09-29T19:24:38Z2022-10-11T07:33:56Zsmol: A Python package for cluster expansions and beyondacceptedv0.0.12022-04-27 17:23:30 UTC772022-09-29 19:24:38 UTC720224504LuisBarroso-LuqueDepartment of Materials Science and Engineering, University of California Berkeley, Berkeley CA, 94720, USA, Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley CA, 94720, USA0000-0002-6453-9545JuliaH.YangDepartment of Materials Science and Engineering, University of California Berkeley, Berkeley CA, 94720, USA, Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley CA, 94720, USA0000-0002-5713-2288FengyuXieDepartment of Materials Science and Engineering, University of California Berkeley, Berkeley CA, 94720, USA, Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley CA, 94720, USA0000-0002-1169-1690TinaChenDepartment of Materials Science and Engineering, University of California Berkeley, Berkeley CA, 94720, USA, Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley CA, 94720, USA0000-0003-0254-8339RonaldL.KamDepartment of Materials Science and Engineering, University of California Berkeley, Berkeley CA, 94720, USA, Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley CA, 94720, USAZinabJadidiDepartment of Materials Science and Engineering, University of California Berkeley, Berkeley CA, 94720, USA, Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley CA, 94720, USAPeichenZhongDepartment of Materials Science and Engineering, University of California Berkeley, Berkeley CA, 94720, USA, Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley CA, 94720, USA0000-0003-1921-1628GerbrandCederDepartment of Materials Science and Engineering, University of California Berkeley, Berkeley CA, 94720, USA, Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley CA, 94720, USA0000-0001-9275-360510.21105/joss.04504https://doi.org/10.5281/zenodo.7115050Python, Cythonhttps://joss.theoj.org/papers/10.21105/joss.04504.pdfcomputational materials science, lattice models, cluster expansion method, Monte Carlo