docs: fix rendering

docs/make.jl

@@ -13,7 +13,7 @@ makedocs(sitename = "NeuralPDE.jl",
          authors = "#",
          modules = [NeuralPDE],
          clean = true, doctest = false, linkcheck = true,
-         warnonly = [:missing_docs, :example_block],
+         warnonly = [:missing_docs],
          format = Documenter.HTML(assets = ["assets/favicon.ico"],
                                   canonical = "https://docs.sciml.ai/NeuralPDE/stable/"),
          pages = pages)

docs/src/tutorials/dgm.md

@@ -28,15 +28,15 @@ where $\vec{x}$ is the concatenated vector of $(t, x)$ and $L$ is the number of
 
 Let's try to solve the following Burger's equation using Deep Galerkin Method for $\alpha = 0.05$ and compare our solution with the finite difference method:
 
-$$
+```math
 \partial_t u(t, x) + u(t, x) \partial_x u(t, x) - \alpha \partial_{xx} u(t, x) = 0 
-$$
+```
 
 defined over
 
-$$ 
+```math
 t \in [0, 1], x \in [-1, 1] 
-$$
+```
 
 with boundary conditions
 ```math

src/BPINN_ode.jl

@@ -123,18 +123,19 @@ function BNNODE(chain, Kernel = HMC; strategy = nothing, draw_samples = 2000,
 end
 
 """
-Contains ahmc_bayesian_pinn_ode() function output:
-1> a MCMCChains.jl chain object for sampled parameters
-2> The set of all sampled parameters
-3> statistics like: 
-    > n_steps
-    > acceptance_rate
-    > log_density
-    > hamiltonian_energy
-    > hamiltonian_energy_error 
-    > numerical_error
-    > step_size
-    > nom_step_size
+Contains `ahmc_bayesian_pinn_ode()` function output:
+
+1. A MCMCChains.jl chain object for sampled parameters.
+2. The set of all sampled parameters.
+3. Statistics like:
+    - n_steps
+    - acceptance_rate
+    - log_density
+    - hamiltonian_energy
+    - hamiltonian_energy_error
+    - numerical_error
+    - step_size
+    - nom_step_size
 """
 struct BPINNstats{MC, S, ST}
     mcmc_chain::MC
@@ -143,10 +144,11 @@ struct BPINNstats{MC, S, ST}
 end
 
 """
-BPINN Solution contains the original solution from AdvancedHMC.jl sampling(BPINNstats contains fields related to that)
-> ensemblesol is the Probabilistic Estimate(MonteCarloMeasurements.jl Particles type) of Ensemble solution from All Neural Network's(made using all sampled parameters) output's.
-> estimated_nn_params - Probabilistic Estimate of NN params from sampled weights,biases
-> estimated_de_params - Probabilistic Estimate of DE params from sampled unknown DE parameters
+BPINN Solution contains the original solution from AdvancedHMC.jl sampling (BPINNstats contains fields related to that).
+
+1. `ensemblesol` is the Probabilistic Estimate (MonteCarloMeasurements.jl Particles type) of Ensemble solution from All Neural Network's (made using all sampled parameters) output's.
+2. `estimated_nn_params` - Probabilistic Estimate of NN params from sampled weights, biases.
+3. `estimated_de_params` - Probabilistic Estimate of DE params from sampled unknown DE parameters.
 """
 struct BPINNsolution{O <: BPINNstats, E, NP, OP, P}
     original::O

src/advancedHMC_MCMC.jl

@@ -344,8 +344,8 @@ end
 
 !!! warn
 
-    Note that ahmc_bayesian_pinn_ode() only supports ODEs which are written in the out-of-place form, i.e.
-    `du = f(u,p,t)`, and not `f(du,u,p,t)`. If not declared out-of-place, then the ahmc_bayesian_pinn_ode()
+    Note that `ahmc_bayesian_pinn_ode()` only supports ODEs which are written in the out-of-place form, i.e.
+    `du = f(u,p,t)`, and not `f(du,u,p,t)`. If not declared out-of-place, then the `ahmc_bayesian_pinn_ode()`
     will exit with an error.
 
 ## Example