Did suggested changes to dae_solve and ode_solve

SciML · Jul 14, 2024 · 08a02c4 · 08a02c4
1 parent 7d26f4c
commit 08a02c4
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diff --git a/src/dae_solve.jl b/src/dae_solve.jl
@@ -88,13 +88,6 @@ function inner_loss(phi::ODEPhi{C, T, U}, f, autodiff::Bool, t::AbstractVector,
     sum(abs2, loss) / length(t)
 end
 
-#=
-function inner_loss(phi::ODEPhi{C, T, U}, f, autodiff::Bool, t::Number, θ,
-    p, differential_vars::AbstractVector) where {C, T, U}
-    sum(abs2, dfdx(phi, t, θ, autodiff,differential_vars) .- f(phi(t, θ), t))
-end
-=#
-
 function inner_loss(phi::ODEPhi{C, T, U}, f, autodiff::Bool, t::Number, θ,
     p, differential_vars::AbstractVector) where {C, T, U}
     dphi = dfdx(phi, t, θ, autodiff,differential_vars)

diff --git a/src/ode_solve.jl b/src/ode_solve.jl
@@ -225,7 +225,6 @@ end
 
 Representation of the loss function, parametric on the training strategy `strategy`.
 """
-
 function generate_loss(strategy::QuadratureTraining, phi, f, autodiff::Bool, tspan, p,
         batch, param_estim::Bool)
     integrand(t::Number, θ) = abs2(inner_loss(phi, f, autodiff, t, θ, p, param_estim))
@@ -306,7 +305,6 @@ function generate_loss(
 end
 
 
-
 function evaluate_tstops_loss(phi, f, autodiff::Bool, tstops, p, batch, param_estim::Bool)
     function loss(θ, _)
         if batch
@@ -322,7 +320,6 @@ function generate_loss(strategy::QuasiRandomTraining, phi, f, autodiff::Bool, ts
     error("QuasiRandomTraining is not supported by NNODE since it's for high dimensional spaces only. Use StochasticTraining instead.")
 end
 
-
 struct NNODEInterpolation{T <: ODEPhi, T2}
     phi::T
     θ::T2