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A modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations

  • Updated Feb 28, 2021
  • Julia
korsbo commented Jul 17, 2018

We really should be testing each and every feature of Latexify and currently we are not.

Test generation is made easy by a macro that we supply:

using Latexify
@Latexify.generate_test latexify("x/y")

generates a test and puts it in your clipboard to be pasted:

@test latexify("x/y") == 

One just have to make sure that the test does ac

Extension functionality which uses Stan.jl, DynamicHMC.jl, and Turing.jl to estimate the parameters to differential equations and perform Bayesian probabilistic scientific machine learning

  • Updated Feb 11, 2021
  • Julia
paulstapor commented Jul 16, 2020

Currently, values for the llh, gradient, the computed trajectories of states and observables, or the sensitivities are checked in unit tests.
Unfortunately, some bugs, such as incorrect Jacobians or switched minus signs in the Newton solver, will not necessarily affect those quantities. However, they will substantially impact solver performance, by causing way too many steps to be taken. Hence, w

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