diff --git a/.github/workflows/CI.yml b/.github/workflows/CI.yml
index 0ee6ec875..84b168a22 100644
--- a/.github/workflows/CI.yml
+++ b/.github/workflows/CI.yml
@@ -14,7 +14,7 @@ jobs:
       matrix:
         group:
           #fixes 682
-          -ODEBPINN
+          - ODEBPINN
 
           - NNPDE1
           - NNPDE2
diff --git a/src/advancedHMC_MCMC.jl b/src/advancedHMC_MCMC.jl
index 4f5c91bb0..3558dfefe 100644
--- a/src/advancedHMC_MCMC.jl
+++ b/src/advancedHMC_MCMC.jl
@@ -134,7 +134,6 @@ end
 function generate_Tar(chain::Flux.Chain, init_params::Nothing)
     θ, re = Flux.destructure(chain)
     # find_good_stepsize,phasepoint takes only float64
-    θ = collect(Float64, θ)
     return θ, re, nothing
 end
 
@@ -193,9 +192,10 @@ function physloglikelihood(Tar::LogTargetDensity, θ)
     p = Tar.prob.p
     dt = Tar.physdt
     if isempty(Tar.dataset[end])
-        t = collect(Float64, Tar.prob.tspan[1]:dt:Tar.prob.tspan[2])
+        t = collect(eltype(dt), Tar.prob.tspan[1]:dt:Tar.prob.tspan[2])
     else
-        t = vcat(collect(Float64, Tar.prob.tspan[1]:dt:Tar.prob.tspan[2]), Tar.dataset[end])
+        t = vcat(collect(eltype(dt), Tar.prob.tspan[1]:dt:Tar.prob.tspan[2]),
+                 Tar.dataset[end])
     end
 
     # parameter estimation chosen or not
@@ -309,12 +309,11 @@ end
 
 function ahmc_bayesian_pinn_ode(prob::DiffEqBase.DEProblem, chain;
                                 dataset = [[]],
-                                # dataset::Vector{Vector{Float64}}
                                 init_params = nothing, nchains = 1,
                                 draw_samples = 1000, l2std = [0.05],
                                 phystd = [0.05], priorsNNw = (0.0, 2.0),
                                 param = [],
-                                autodiff = false, physdt = 1 / 20.0f0,
+                                autodiff = false, physdt = 1 / 20.0,
                                 Proposal = StaticTrajectory,
                                 Adaptor = StanHMCAdaptor, targetacceptancerate = 0.8,
                                 Integrator = Leapfrog,
@@ -338,12 +337,13 @@ function ahmc_bayesian_pinn_ode(prob::DiffEqBase.DEProblem, chain;
         throw(error("number of chains must be greater than 1"))
     end
 
-    # 
+    # eltype(physdt) cause needs Float64 for find_good_stepsize
     if chain isa Lux.AbstractExplicitLayer
-        # Lux chain(using component array later as vector_to_parameter need namedtuple,AHMC uses Float64)
-        initial_θ = collect(Float64, vcat(ComponentArrays.ComponentArray(initial_nnθ)))
+        # Lux chain(using component array later as vector_to_parameter need namedtuple)
+        initial_θ = collect(eltype(physdt),
+                            vcat(ComponentArrays.ComponentArray(initial_nnθ)))
     else
-        initial_θ = initial_nnθ
+        initial_θ = collect(eltype(physdt), initial_nnθ)
     end
 
     # adding ode parameter estimation