changes-2

src/BPINN_ode.jl

@@ -26,7 +26,7 @@ of the physics-informed neural network which is used as a solver for a standard
 * `Kernel`: Choice of MCMC Sampling Algorithm. Defaults to `AdvancedHMC.HMC`
 
 ## Keyword Arguments
-(refer ahmc_bayesian_pinn_ode() keyword arguments.)
+(refer `NeuralPDE.ahmc_bayesian_pinn_ode` keyword arguments.)
 
 ## Example
 

src/PDE_BPINN.jl

@@ -327,15 +327,10 @@ ahmc_bayesian_pinn_pde(pde_system, discretization;
 * `nchains`: number of chains you want to sample
 
 # AdvancedHMC.jl is still developing convenience structs so might need changes on new releases.
-* `Kernel`: Choice of MCMC Sampling Algorithm object with appropriat args/kwargs (AdvancedHMC.jl implemenations HMC/NUTS/HMCDA).
-* `Adaptorkwargs`: A NamedTuple containing the chosen Adaptor, it's Metric and targetacceptancerate, as follows :
-    * `Adaptor`: https://turinglang.org/AdvancedHMC.jl/stable/
-    * `Metric`: https://turinglang.org/AdvancedHMC.jl/stable/
-    * `targetacceptancerate`: Target percentage(in decimal) of iterations in which the proposals were accepted(0.8 by default)
-* `Integratorkwargs`: A NamedTuple containing the chosen integrator and its keyword Arguments, as follows :
-    * `Integrator`: https://turinglang.org/AdvancedHMC.jl/stable/
-    * `jitter_rate`: https://turinglang.org/AdvancedHMC.jl/stable/
-    * `tempering_rate`: https://turinglang.org/AdvancedHMC.jl/stable/
+* `Kernel`: Choice of MCMC Sampling Algorithm object HMC/NUTS/HMCDA (AdvancedHMC.jl implemenations ).
+* `Adaptorkwargs`: `Adaptor`, `Metric`, `targetacceptancerate`. Refer: https://turinglang.org/AdvancedHMC.jl/stable/
+   Note: Target percentage(in decimal) of iterations in which the proposals are accepted (0.8 by default)
+* `Integratorkwargs`: `Integrator`, `jitter_rate`, `tempering_rate`. Refer: https://turinglang.org/AdvancedHMC.jl/stable/
 * `saveats`: Grid spacing for each independant variable for evaluation of ensemble solution, estimated parameters.
 * `numensemble`: Number of last samples to take for creation of ensemble solution, estimated parameters.
 * `progress`: controls whether to show the progress meter or not.

src/advancedHMC_MCMC.jl

@@ -448,20 +448,16 @@ Incase you are only solving the Equations for solution, do not provide dataset
 
 # AdvancedHMC.jl is still developing convenience structs so might need changes on new releases.
 * `Kernel`: Choice of MCMC Sampling Algorithm (AdvancedHMC.jl implemenations HMC/NUTS/HMCDA)
-* `Integratorkwargs`: A NamedTuple containing the chosen integrator and its keyword Arguments, as follows :
-    * `Integrator`: https://turinglang.org/AdvancedHMC.jl/stable/
-    * `jitter_rate`: https://turinglang.org/AdvancedHMC.jl/stable/
-    * `tempering_rate`: https://turinglang.org/AdvancedHMC.jl/stable/
-* `Adaptorkwargs`: A NamedTuple containing the chosen Adaptor, it's Metric and targetacceptancerate, as follows :
-    * `Adaptor`: https://turinglang.org/AdvancedHMC.jl/stable/
-    * `Metric`: https://turinglang.org/AdvancedHMC.jl/stable/
-    * `targetacceptancerate`: Target percentage(in decimal) of iterations in which the proposals were accepted(0.8 by default)
+* `Integratorkwargs`: `Integrator`, `jitter_rate`, `tempering_rate`. Refer: https://turinglang.org/AdvancedHMC.jl/stable/
+* `Adaptorkwargs`: `Adaptor`, `Metric`, `targetacceptancerate`. Refer: https://turinglang.org/AdvancedHMC.jl/stable/
+    Note: Target percentage(in decimal) of iterations in which the proposals are accepted (0.8 by default)
 * `MCMCargs`: A NamedTuple containing all the chosen MCMC kernel's(HMC/NUTS/HMCDA) Arguments, as follows :
     * `n_leapfrog`: number of leapfrog steps for HMC
     * `δ`: target acceptance probability for NUTS and HMCDA
     * `λ`: target trajectory length for HMCDA
     * `max_depth`: Maximum doubling tree depth (NUTS)
     * `Δ_max`: Maximum divergence during doubling tree (NUTS)
+    Refer: https://turinglang.org/AdvancedHMC.jl/stable/
 * `progress`: controls whether to show the progress meter or not.
 * `verbose`: controls the verbosity. (Sample call args in AHMC)