SciML Scientific Machine Learning Software Org
@SciML_Org
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Open source software for scientific machine learning (#sciml) in #julialang #python #rstats.
Joined June 2020
We are happy to announce a major advance in DifferentialEquations.jl. With improvements to #juilalang BDF methods, the QNDF method outperforms CVODE on the full SciMLBenchmarks suite and has replaced it in recommendations and defaults. Read more: https://t.co/tpfxAmlWSr
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Shapley values, a method of explainable Machine Learning (ML), are now in GlobalSensitivity.jl #julialang #sciml. Shapley Effects provide a robust methodology for quantifying the influence of input variables on model outputs. Shapley on Neural ODEs: https://t.co/1x4IsbIOum.
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Overhauled error messages make it easy to understand incorrect inputs. Major expansions to docstrings, consistent use of style guides, and new #julialang packages for neural operators (DeepONets, Fourier Neural Operators)? See the #sciml ecosystem update! https://t.co/RSok0i1Pdl
sciml.ai
Open Source Software for Scientific Machine Learning
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Are you interested in @SciMLCon 2023? Well we are too, and we're interested in finding out what would make it even better than last year! Please take this survey to share your thoughts: https://t.co/JfS14Whfer
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New paper by Vaibhav Dixit and Chris Rackauckas on a #julialang #sciml library for Global Sensitivity Analysis (GSA)!
Just published in JOSS: 'GlobalSensitivity.jl: Performant and Parallel Global Sensitivity Analysis with Julia'
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SciML@JuliaCon 2022 Modeling of Chemical Reaction Networks using Catalyst.jl | JuliaCon 2022 https://t.co/g0mlw3drO0
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SciML@JuliaCon 2022 Differentiable Earth system models in Julia | JuliaCon 2022 | Ludovic Räss, Boris Kaus, Julien Le Sommer, Nora Loose, Mathieu Morlighem, Chris Hill, Sarah Williamson, Valentin/Billy, Michel/Krishna, Chris Rackauckas https://t.co/BJiTpoDyBc
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SciML@JuliaCon 2022 FdeSolver.jl: Solving fractional differential equations | Moein Khalighi | JuliaCon 2022 https://t.co/sEdaQS6gJu
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SciML@JuliaCon 2022 Automated PDE Solving in Julia with MethodOfLines.jl | Alex Jones | JuliaCon 2022 https://t.co/nMLVqVE7o3
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SciML@JuliaCon 2022 Modeling Instron's Crash Simulation System with ModelingToolkit.jl | Bradley Carman | JuliaCon 2022 https://t.co/XWUlaSPSmZ
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SciML@JuliaCon 2022 How to recover models from data using DataDrivenDiffEq.jl | Carl Julius Martensen | JuliaCon 2022 https://t.co/bwjbC67Vhy
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SciML@JuliaCon 2022 GeneDrive.jl: Simulate and Optimize Biological Interventions | Valeri Vasquez | JuliaCon 2022 https://t.co/gIsrpW2FVN
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SciML@JuliaCon 2022 Fast optimization via randomized numerical linear algebra | Theo Diamandis | JuliaCon 2022 https://t.co/bhalF9D9xE
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SciML@JuliaCon 2022 How to debug Julia simulation codes (ODEs, optimization, etc.!) | ChrisRackauckas | JuliaCon 2022 https://t.co/iKdpR6qq70
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SciML@JuliaCon 2022 Exploring audio circuits with ModelingToolkit.jl | George Gkountouras | JuliaCon 2022 https://t.co/C9KpzOmi24
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SciML@JuliaCon 2022 Using SciML to predict the time evolution of a complex network. | Andre Macleod | JuliaCon 2022 https://t.co/XcSforH7B4
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SciML@JuliaCon 2022 Universal Differential Equation models with wrong assumptions | Luca Reale | JuliaCon 2022 https://t.co/1SVAF9v0eX
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SciML@JuliaCon 2022 Lux.jl: Explicit Parameterization of Neural Networks in Julia | Avik Pal | JuliaCon 2022 https://t.co/CmTSXR5weN
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SciML@JuliaCon 2022 Adaptive Radial Basis Function Surrogates in Julia | Ranjan Anantharaman | JuliaCon 2022 https://t.co/6TNXziC4rB
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SciML@JuliaCon 2022 HighDimPDE.jl: A Julia package for solving high-dimensional PDEs | Victor Boussange | JuliaCon 2022 https://t.co/XTkr44w1dA
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SciML@JuliaCon 2022 LinearSolve.jl: because A\b is not good enough | ChrisRackauckas | JuliaCon 2022 https://t.co/0c0G2g5GDo
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