tps://docs.sciml.ai/DiffEqParamEstim/stable/">DiffEqParamEstim.jl</a> and <a href="https://docs.sciml.ai/DiffEqBayes/stable/">DiffEqBayes.jl</a> provide classical maximum likelihood and Bayesian estimation for differential equation based models, while <a href="https://docs.sciml.ai/SciMLSensitivity/dev/">SciMLSensitivity.jl</a> enables the deep hand-optimized methods for forward and adjoint senstivity (i.e. derivatives) of equation solvers. This enables the training of embedded neural networks inside of dif