Allowing a function to be called multiple times with different inputs

src/symbolic_utilities.jl

@@ -460,21 +460,21 @@ function get_argument(eqs, _indvars::Array, _depvars::Array)
     get_argument(eqs, dict_indvars, dict_depvars)
 end
 function get_argument(eqs, dict_indvars, dict_depvars)
-    """Equations, as expressions"""
+    "Equations, as expressions"
     exprs = toexpr.(eqs)
-    """Instances of each dependent variable that appears in the expression, by dependent variable, by equation"""
+    "Instances of each dependent variable that appears in the expression, by dependent variable, by equation"
     vars = map(exprs) do expr # For each equation,...
-        """Arrays of instances of each dependent variable, by dependent variable"""
+        "Arrays of instances of each dependent variable, by dependent variable"
         _vars = map(depvar -> find_thing_in_expr(expr, depvar), collect(keys(dict_depvars)))
-        """Arrays of instances of each dependent variable that appears in the expression, by dependent variable"""
+        "Arrays of instances of each dependent variable that appears in the expression, by dependent variable"
         f_vars = filter(x -> !isempty(x), _vars)
     end
     #    vars = [depvar for expr in vars for depvar in expr ]
     args_ = map(vars) do _vars
-        """Arguments of all instances of dependent variable, by instance, by dependent variable"""
+        "Arguments of all instances of dependent variable, by instance, by dependent variable"
         ind_args_ = map.(var -> var.args[2:end], _vars)
         syms = Set{Symbol}()
-        """All arguments in any instance of a dependent variable"""
+        "All arguments in any instance of a dependent variable"
         all_ind_args = vcat((ind_args_...)...)
 
         # Add any independent variables from expression dependent variable calls

test/NNPDE_tests.jl

@@ -352,7 +352,7 @@ domains = [x ∈ Interval(0.0, 1.0)]
 chain = [[Lux.Chain(Lux.Dense(1, 12, Lux.tanh), Lux.Dense(12, 12, Lux.tanh),
                     Lux.Dense(12, 1)) for _ in 1:3]
          [Lux.Chain(Lux.Dense(1, 4, Lux.tanh), Lux.Dense(4, 1)) for _ in 1:2]]
-quasirandom_strategy = NeuralPDE.QuasiRandomTraining(130; #points
+quasirandom_strategy = NeuralPDE.QuasiRandomTraining(200; #points
                                                      sampling_alg = LatinHypercubeSample())
 
 discretization = NeuralPDE.PhysicsInformedNN(chain, quasirandom_strategy)
@@ -372,7 +372,7 @@ cb_ = function (p, l)
     return false
 end
 
-res = solve(prob, OptimizationOptimJL.BFGS(); maxiters = 1000)
+res = solve(prob, OptimizationOptimJL.BFGS(); maxiters = 1500)
 phi = discretization.phi[1]
 
 analytic_sol_func(x) = (π * x * (-x + (π^2) * (2 * x - 3) + 1) - sin(π * x)) / (π^3)