# ERROR: MethodError: no method matching PhysicsInformedNN(::Float64)

**URL:** <https://discourse.julialang.org/t/error-methoderror-no-method-matching-physicsinformednn-float64/49610>\
**Category:** Machine Learning\
**Created:** [November 5, 2020, 1:55am UTC](https://discourse.julialang.org/t/error-methoderror-no-method-matching-physicsinformednn-float64/49610 "2020-11-05T01:55:00Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![BaoBaoDeMeng](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/baobaodemeng/32/19155_2.png) [@BaoBaoDeMeng](https://discourse.julialang.org/u/BaoBaoDeMeng)\
**Post date:** [November 5, 2020, 1:55am UTC](https://discourse.julialang.org/t/error-methoderror-no-method-matching-physicsinformednn-float64/49610/1 "2020-11-05T01:55:00Z")

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> using Flux, DiffEqFluxModeling,ToolkitDiffEqBase, Plots, NeuralPDETest
> 
> ## Example 1, 1D ode

> # 1D ODE
> 
> eq = Dt(u(t,θ)) ~ t^3 + 2_t + (t^2)_((1+3\*(t^2))/(1+t+(t^3))) - u(t,θ)_(t + ((1+3_(t^2))/(1+t+t^3)))
> 
> # Boundary conditions
> 
> bcs = [u(0.) ~ 1.0 , u(1.) ~ 1.202]
> 
> # Space and time domains
> 
> domains = [t ∈ IntervalDomain(0.0,1.0)]
> 
> # Discretization
> 
> dx = 0.1  
> discretization = PhysicsInformedNN(dx)  
> ERROR: MethodError: no method matching PhysicsInformedNN(::Float64)  
> Closest candidates are:  
> PhysicsInformedNN(::Any, ::Any) at C:\Us\YanWei.julia\packages\NeuralPDE\7SDF6\src\pinns\_pde\_solve.jl:25  
> PhysicsInformedNN(::Any, ::Any, ::Any; \_phi, autodiff, \_derivative, strategy, kwargs…) at  
> [1] top-level scope at REPL[78]:1

How can I solve this problem?  
thanks

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<div class="post-metadata">

**Author:** ![DavidA](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/davida/32/16174_2.png) [@DavidA](https://discourse.julialang.org/u/DavidA)\
**Post date:** [November 5, 2020, 4:37am UTC](https://discourse.julialang.org/t/error-methoderror-no-method-matching-physicsinformednn-float64/49610/2 "2020-11-05T04:37:32Z")

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You need the second input, the description of your network: [https://neuralpde.sciml.ai/dev/examples/ode/](https://neuralpde.sciml.ai/dev/examples/ode/)