update links

Author: prbzrg

CITATION.bib

@@ -16,7 +16,7 @@ @article{DBLP:journals/corr/abs-1902-02376
   journal   = {CoRR},
   volume    = {abs/1902.02376},
   year      = {2019},
-  url       = {http://arxiv.org/abs/1902.02376},
+  url       = {https://arxiv.org/abs/1902.02376},
   archivePrefix = {arXiv},
   eprint    = {1902.02376},
   timestamp = {Tue, 21 May 2019 18:03:36 +0200},
@@ -52,7 +52,7 @@ @article{Flux.jl-2018
   journal   = {CoRR},
   volume    = {abs/1811.01457},
   year      = {2018},
-  url       = {http://arxiv.org/abs/1811.01457},
+  url       = {https://arxiv.org/abs/1811.01457},
   archivePrefix = {arXiv},
   eprint    = {1811.01457},
   timestamp = {Thu, 22 Nov 2018 17:58:30 +0100},

README.md

@@ -12,9 +12,9 @@
 
 DiffEqFlux.jl fuses the world of differential equations with machine learning
 by helping users put diffeq solvers into neural networks. This package utilizes
-[DifferentialEquations.jl](http://docs.sciml.ai/DiffEqDocs/stable/), 
-[Flux.jl](https://docs.sciml.ai/Flux.jl/stable/) and [Lux.jl](https://docs.sciml.ai/Lux/stable/)  as its building blocks to support research in
-[Scientific Machine Learning](http://www.stochasticlifestyle.com/the-essential-tools-of-scientific-machine-learning-scientific-ml/),
+[DifferentialEquations.jl](https://docs.sciml.ai/DiffEqDocs/stable/),
+[Flux.jl](https://docs.sciml.ai/Flux/stable/) and [Lux.jl](https://docs.sciml.ai/Lux/stable/)  as its building blocks to support research in
+[Scientific Machine Learning](https://www.stochasticlifestyle.com/the-essential-tools-of-scientific-machine-learning-scientific-ml/),
 specifically neural differential equations and universal differential equations,
 to add physical information into traditional machine learning.
 
@@ -52,7 +52,7 @@ methods. For examples, please refer to
 [the release blog post](https://julialang.org/blog/2019/01/fluxdiffeq).
 Additional demonstrations, like neural
 PDEs and neural jump SDEs, can be found
-[in this blog post](http://www.stochasticlifestyle.com/neural-jump-sdes-jump-diffusions-and-neural-pdes/)
+[in this blog post](https://www.stochasticlifestyle.com/neural-jump-sdes-jump-diffusions-and-neural-pdes/)
 (among many others!).
 
 Do not limit yourself to the current neuralization. With this package, you can

docs/src/examples/neural_ode.md

@@ -175,7 +175,7 @@ together, we are able to fit the neural ODE in 9 seconds! (Note, the timing
 commented out the plotting). You can easily incorporate the procedure below to
 set up custom optimization problems. For more information on the usage of
 [Optimization.jl](https://github.com/SciML/Optimization.jl), please consult
-[this](http://docs.sciml.ai/Optimization/stable/) documentation.
+[this](https://docs.sciml.ai/Optimization/stable/) documentation.
 
 The `x` and `p` variables in the optimization function are different than
 `x` and `p` above. The optimization function runs over the space of parameters of

docs/src/examples/neural_sde.md

@@ -56,7 +56,7 @@ prob_truesde = SDEProblem(trueSDEfunc, true_noise_func, u0, tspan)
 ```
 
 For our dataset we will use DifferentialEquations.jl's [parallel ensemble
-interface](http://docs.juliadiffeq.org/dev/features/ensemble.html) to generate
+interface](https://docs.sciml.ai/DiffEqDocs/stable/features/ensemble/) to generate
 data from the average of 10,000 runs of the SDE:
 
 ```@example nsde

docs/src/index.md

@@ -1,4 +1,4 @@
-# DiffEqFlux: High Level Pre-Built Architectures for Implicit Deep Learning 
+# DiffEqFlux: High Level Pre-Built Architectures for Implicit Deep Learning
 
 DiffEqFlux.jl is an implicit deep learning library built using the SciML ecosystem. It is
 a high level interface that pulls together all of the tools with heuristics
@@ -18,8 +18,8 @@ and helper functions to make training such deep implicit layer models fast and e
 The approach of this package is the easy and efficient training of
 [Universal Differential Equations](https://arxiv.org/abs/2001.04385).
 DiffEqFlux.jl provides architectures which match the interfaces of
-machine learning libraries such as [Flux.jl](https://fluxml.ai/)
-and [Lux.jl](http://lux.csail.mit.edu/dev/)
+machine learning libraries such as [Flux.jl](https://docs.sciml.ai/Flux/stable/)
+and [Lux.jl](https://docs.sciml.ai/Lux/stable/)
 to make it easy to build continuous-time machine learning layers
 into larger machine learning applications.
 
@@ -88,7 +88,7 @@ Pkg.status(;mode = PKGMODE_MANIFEST) # hide
 </details>
 ```
 ```@raw html
-You can also download the 
+You can also download the
 <a href="
 ```
 ```@eval
@@ -109,4 +109,4 @@ link = "https://github.com/SciML/"*name*".jl/tree/gh-pages/v"*version*"/assets/P
 ```
 ```@raw html
 ">project</a> file.
-```
+```

src/hnn.jl

@@ -15,7 +15,7 @@ particles. It then returns the time derivatives for position and momentum.
 
 To obtain the gradients to train this network, ReverseDiff.gradient is supposed to
 be used. This prevents the usage of `DiffEqFlux.sciml_train` or `Flux.train`. Follow
-this [tutorial](https://diffeqflux.sciml.ai/dev/examples/hamiltonian_nn/) to see how
+this [tutorial](https://docs.sciml.ai/DiffEqFlux/stable/examples/hamiltonian_nn/) to see how
 to define a training loop to circumvent this issue.
 
 ```julia

src/neural_de.jl

@@ -504,7 +504,7 @@ Arguments:
 - `alg`: The algorithm used to solve the ODE. Defaults to `nothing`, i.e. the
   default algorithm from DifferentialEquations.jl. This method requires an
   implicit ODE solver compatible with singular mass matrices. Consult the
-  [DAE solvers](https://diffeq.sciml.ai/latest/solvers/dae_solve/) documentation for more details.
+  [DAE solvers](https://docs.sciml.ai/DiffEqDocs/stable/solvers/dae_solve/) documentation for more details.
 - `sensealg`: The choice of differentiation algorthm used in the backpropogation.
   Defaults to an adjoint method, and with `FastChain` it defaults to utilizing
   a tape-compiled ReverseDiff vector-Jacobian product for extra efficiency. Seee