diff --git a/test/BPINN_Tests.jl b/test/BPINN_Tests.jl
index 75cb40555..f0e71c7ba 100644
--- a/test/BPINN_Tests.jl
+++ b/test/BPINN_Tests.jl
@@ -43,7 +43,7 @@ x̂1 = collect(Float64, Array(u1) + 0.02 * randn(size(u1)))
 time1 = vec(collect(Float64, ta0))
 physsol0_1 = [linear_analytic(prob.u0, p, time1[i]) for i in eachindex(time1)]
 
-chainflux = Flux.Chain(Flux.Dense(1, 7, tanh), Flux.Dense(7, 1))
+chainflux = Flux.Chain(Flux.Dense(1, 7, tanh), Flux.Dense(7, 1)) |> Flux.f64
 chainlux = Lux.Chain(Lux.Dense(1, 7, tanh), Lux.Dense(7, 1))
 init1, re1 = destructure(chainflux)
 θinit, st = Lux.setup(Random.default_rng(), chainlux)
@@ -116,14 +116,11 @@ x̂1 = collect(Float64, Array(u1) + 0.02 * randn(size(u1)))
 time1 = vec(collect(Float64, ta0))
 physsol1_1 = [linear_analytic(prob.u0, p, time1[i]) for i in eachindex(time1)]
 
-chainflux1 = Flux.Chain(Flux.Dense(1, 7, tanh), Flux.Dense(7, 1))
+chainflux1 = Flux.Chain(Flux.Dense(1, 7, tanh), Flux.Dense(7, 1)) |> Flux.f64
 chainlux1 = Lux.Chain(Lux.Dense(1, 7, tanh), Lux.Dense(7, 1))
 init1, re1 = destructure(chainflux1)
 θinit, st = Lux.setup(Random.default_rng(), chainlux1)
 
-# weak priors call for larger NNs? 
-# my L2 loss also works(binds parameters)?
-
 fh_mcmc_chain1, fhsamples1, fhstats1 = ahmc_bayesian_pinn_ode(prob, chainflux1,
     dataset = dataset,
     draw_samples = 2500,
@@ -197,6 +194,7 @@ meanscurve2 = prob.u0 .+ (t .- prob.tspan[1]) .* luxmean
 @test mean(abs.(physsol1_1 .- sol2flux.ensemblesol[1])) < 8e-2
 @test mean(abs.(x̂1 .- sol2lux.ensemblesol[1])) < 8e-2
 @test mean(abs.(physsol1_1 .- sol2lux.ensemblesol[1])) < 8e-2
+
 # ESTIMATED ODE PARAMETERS (NN1 AND NN2)
 @test abs(p - sol2flux.estimated_ode_params[1]) < abs(0.1 * p)
 @test abs(p - sol2lux.estimated_ode_params[1]) < abs(0.1 * p)
@@ -224,7 +222,7 @@ time1 = vec(collect(Float64, ta0))
 physsol2 = [linear_analytic(prob.u0, p, time1[i]) for i in eachindex(time1)]
 
 chainflux12 = Flux.Chain(Flux.Dense(1, 6, tanh), Flux.Dense(6, 6, tanh),
-    Flux.Dense(6, 1))
+    Flux.Dense(6, 1)) |> Flux.f64
 chainlux12 = Lux.Chain(Lux.Dense(1, 6, tanh), Lux.Dense(6, 6, tanh), Lux.Dense(6, 1))
 init1, re1 = destructure(chainflux12)
 θinit, st = Lux.setup(Random.default_rng(), chainlux12)