Code updates

Author: ParamThakkar123

benchmarks/PINNErrorsVsTime/allen_cahn_et.jmd

@@ -20,7 +20,7 @@ using DelimitedFiles
 using QuasiMonteCarlo, Random
 import ModelingToolkit: Interval, infimum, supremum
 
-function allen_cahn(strategy, minimizer, maxIters)
+function allen_cahn(strategy, minimizer, maxIters, time_limit=3600.0)  # Added time_limit parameter (default 1 hour in seconds)
     ##  DECLARATIONS
     @parameters t x1 x2 x3 x4
     @variables u(..)
@@ -113,7 +113,7 @@ function allen_cahn(strategy, minimizer, maxIters)
     timeCounter = 0.0
     startTime = time_ns()
 
-    # Callback function
+    # Callback function with time cap
     cb = function (p, l)
         try
             deltaT_s = time_ns()
@@ -124,6 +124,12 @@ function allen_cahn(strategy, minimizer, maxIters)
             push!(error, l)
 
             timeCounter += time_ns() - deltaT_s
+            
+            if (ctime / 1e9 > time_limit)
+                println("Time limit of $time_limit seconds exceeded. Stopping training.")
+                return true
+            end
+            
             return false
         catch e
             @warn "Callback error: $e"
@@ -161,10 +167,10 @@ end
 maxIters = [(1,1,1,1,1,1,1000),(1,1,1,1,300,300,300)] #iters for ADAM/LBFGS
 # maxIters = [(1,1,1,1,1,1,10),(1,1,1,3,3,3,3)] #iters for ADAM/LBFGS
 
-strategies = [NeuralPDE.QuadratureTraining(quadrature_alg = CubaCuhre(), reltol = 1e-4, abstol = 1e-4, maxiters = 1100),
-              NeuralPDE.QuadratureTraining(quadrature_alg = HCubatureJL(), reltol = 1e-4, abstol = 1e-4, maxiters = 1100, batch = 0),
-              NeuralPDE.QuadratureTraining(quadrature_alg = CubatureJLh(), reltol = 1e-4, abstol = 1e-4, maxiters = 1100),
-              NeuralPDE.QuadratureTraining(quadrature_alg = CubatureJLp(), reltol = 1e-4, abstol = 1e-4, maxiters = 1100),
+strategies = [NeuralPDE.QuadratureTraining(quadrature_alg = CubaCuhre(), reltol = 1e-4, abstol = 1e-4, maxiters = 100),
+              NeuralPDE.QuadratureTraining(quadrature_alg = HCubatureJL(), reltol = 1e-4, abstol = 1e-4, maxiters = 100, batch = 0),
+              NeuralPDE.QuadratureTraining(quadrature_alg = CubatureJLh(), reltol = 1e-4, abstol = 1e-4, maxiters = 100),
+              NeuralPDE.QuadratureTraining(quadrature_alg = CubatureJLp(), reltol = 1e-4, abstol = 1e-4, maxiters = 100),
               NeuralPDE.GridTraining(0.2),
               NeuralPDE.StochasticTraining(400 ; bcs_points= 50),
               NeuralPDE.QuasiRandomTraining(400 ; bcs_points= 50)]
@@ -194,15 +200,15 @@ losses_res = Dict()
 ## Convergence
 
 for min =1:length(minimizers) # minimizer
-      for strat=1:length(strategies) # strategy
-            # println(string(strategies_short_name[strat], "  ", minimizers_short_name[min]))
-            res = allen_cahn(strategies[strat], minimizers[min], maxIters[min][strat])
-            push!(error_res, string(strat,min)     => res[1])
-            push!(params_res, string(strat,min) => res[2])
-            push!(domains, string(strat,min)        => res[3])
-            push!(times, string(strat,min)        => res[4])
-            push!(losses_res, string(strat,min)        => res[5])
-      end
+    for strat=1:length(strategies) # strategy
+          # println(string(strategies_short_name[strat], "  ", minimizers_short_name[min]))
+          res = allen_cahn(strategies[strat], minimizers[min], maxIters[min][strat])
+          push!(error_res, string(strat,min)     => res[1])
+          push!(params_res, string(strat,min) => res[2])
+          push!(domains, string(strat,min)        => res[3])
+          push!(times, string(strat,min)        => res[4])
+          push!(losses_res, string(strat,min)        => res[5])
+    end
 end
 ```