name change: RSWAF1D -> TanhKernel1D. also figured out splitting of T…

…anh kernels. Also need to attempt boosted training.

experiments_NGN/AD1D/run.jl

@@ -26,21 +26,20 @@ data_kws = (; Ix = :, It = :)
 # modeldir  = joinpath(@__DIR__, "dump_dnn")
 
 #------------------------------------------------------#
-# RSWAF
+# Tanh Kernels
 #------------------------------------------------------#
 # data_kws = (; Ix = LinRange(1, 512, 64), It = LinRange(1, 500, 500))
 # data_kws = map(x -> round.(Int, x), data_kws)
 
-train_params  = (; type = :RSWAF)
+train_params  = (;)
 evolve_params  = (; scheme = :GalerkinCollocation)
-# evolve_params = (; timealg = RungeKutta4())#, Δt = 1e-3, adaptive = false)
 
-makemodel = makemodelGaussian
+makemodel = makemodelTanh
 modelfilename = "model_05.jld2"
-modeldir  = joinpath(@__DIR__, "dump_rswaf")
+modeldir  = joinpath(@__DIR__, "dump_tanh")
 
 #------------------------------------------------------#
-# Gaussian
+# Gaussian Kernels
 #------------------------------------------------------#
 # data_kws = (; Ix = LinRange(1, 512, 64), It = LinRange(1, 500, 500))
 # data_kws = map(x -> round.(Int, x), data_kws)
@@ -54,7 +53,7 @@ modeldir  = joinpath(@__DIR__, "dump_rswaf")
 # modeldir  = joinpath(@__DIR__, "dump_gaussian")
 
 #------------------------------------------------------#
-# Gaussian (exact)
+# Gaussian (exact IC)
 #------------------------------------------------------#
 # data_kws = (; Ix = LinRange(1, 256, 64), It = LinRange(1, 500, 500))
 # data_kws = map(x -> round.(Int, x), data_kws)
@@ -104,24 +103,5 @@ for case in 5:7
     # sleep(2)
 end
 
-#======================================================#
-#
-# ARCHITECTURE
-# - Check out multiplicative feature networks.
-#   Maybe they can speed-up SDF type problems.
-#
-# GAUSSIAN REFINEMENT/CULLING
-# - 
-#
-# HYPER-REDUCTION
-# - Each Gaussian needs ~5 points to be evolved properly. This should be
-#   helpful in hyper-reduction. We should do local sampling around each
-#   Gaussian. That is: uniformly pick 5 x ∈ [x̄ - 2σ, x̄ + 2σ]
-#
-# LITERATURE
-# - Check out Gaussian process literature
-#
-# NEW CONTRIB
-# - Make parameterization probabilistic. Then you get UQ for free.
 #======================================================#
 nothing

experiments_NGN/Burg1D/dump_tanh/project1/split0/statistics.txt

@@ -0,0 +1,110 @@
+CHECKPOINT 01
+Epoch [0 / 0]	TRAIN LOSS: 0.00016998869 || TEST LOSS: 0.00016998869
+#======================#
+TRAIN STATS
+R² score:             0.99863902
+MSE (mean SQR error): 0.00016998869
+RMSE (Root MSE):      0.013037971
+MAE (mean ABS error): 0.0098726759
+maxAE (max ABS error) 0.034802139
+Lipschitz bound:      1.0
+
+#======================#
+#======================#
+TEST  STATS
+R² score:             0.99863902
+MSE (mean SQR error): 0.00016998869
+RMSE (Root MSE):      0.013037971
+MAE (mean ABS error): 0.0098726759
+maxAE (max ABS error) 0.034802139
+Lipschitz bound:      1.0
+
+#======================#
+CHECKPOINT 02
+Epoch [0 / 0]	TRAIN LOSS: 0.00016858064 || TEST LOSS: 0.00016858064
+#======================#
+TRAIN STATS
+R² score:             0.99865213
+MSE (mean SQR error): 0.00016858064
+RMSE (Root MSE):      0.012983861
+MAE (mean ABS error): 0.010191589
+maxAE (max ABS error) 0.033603411
+Lipschitz bound:      1.0
+
+#======================#
+#======================#
+TEST  STATS
+R² score:             0.99865213
+MSE (mean SQR error): 0.00016858064
+RMSE (Root MSE):      0.012983861
+MAE (mean ABS error): 0.010191589
+maxAE (max ABS error) 0.033603411
+Lipschitz bound:      1.0
+
+#======================#
+CHECKPOINT 03
+Epoch [0 / 0]	TRAIN LOSS: 0.00016835895 || TEST LOSS: 0.00016835895
+#======================#
+TRAIN STATS
+R² score:             0.99865061
+MSE (mean SQR error): 0.00016835895
+RMSE (Root MSE):      0.012975321
+MAE (mean ABS error): 0.010319232
+maxAE (max ABS error) 0.033478078
+Lipschitz bound:      1.0
+
+#======================#
+#======================#
+TEST  STATS
+R² score:             0.99865061
+MSE (mean SQR error): 0.00016835895
+RMSE (Root MSE):      0.012975321
+MAE (mean ABS error): 0.010319232
+maxAE (max ABS error) 0.033478078
+Lipschitz bound:      1.0
+
+#======================#
+CHECKPOINT 04
+Epoch [0 / 0]	TRAIN LOSS: 0.00016817819 || TEST LOSS: 0.00016817819
+#======================#
+TRAIN STATS
+R² score:             0.99865308
+MSE (mean SQR error): 0.00016817819
+RMSE (Root MSE):      0.012968353
+MAE (mean ABS error): 0.01027797
+maxAE (max ABS error) 0.033458792
+Lipschitz bound:      1.0
+
+#======================#
+#======================#
+TEST  STATS
+R² score:             0.99865308
+MSE (mean SQR error): 0.00016817819
+RMSE (Root MSE):      0.012968353
+MAE (mean ABS error): 0.01027797
+maxAE (max ABS error) 0.033458792
+Lipschitz bound:      1.0
+
+#======================#
+CHECKPOINT 05
+Epoch [0 / 0]	TRAIN LOSS: 0.00016815641 || TEST LOSS: 0.00016815641
+#======================#
+TRAIN STATS
+R² score:             0.99865294
+MSE (mean SQR error): 0.00016815641
+RMSE (Root MSE):      0.012967514
+MAE (mean ABS error): 0.010268515
+maxAE (max ABS error) 0.033389043
+Lipschitz bound:      1.0
+
+#======================#
+#======================#
+TEST  STATS
+R² score:             0.99865294
+MSE (mean SQR error): 0.00016815641
+RMSE (Root MSE):      0.012967514
+MAE (mean ABS error): 0.010268515
+maxAE (max ABS error) 0.033389043
+Lipschitz bound:      1.0
+
+#======================#