DM ensembles: same-L networks only, 2D L=16..128 and 3D L=64

DM/2D/{phi4_2D_L128_kappa0.2705_lambda0.022_DM_trainL8-16-32-64.npy → phi4_2D_L16_kappa0.2705_lambda0.022_DM.npy}

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DM/{3D/phi4_3D_L64_kappa0.1923_lambda0.9_DM_trainL4-8-16-32.npy → 2D/phi4_2D_L32_kappa0.2705_lambda0.022_DM.npy}

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DM/{3D/phi4_3D_L64_kappa0.1923_lambda0.9_DM_trainL4-8-16-32_MALAexact.npy → 2D/phi4_2D_L64_kappa0.2705_lambda0.022_DM.npy}

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DM/3D/phi4_3D_L64_kappa0.1923_lambda0.9_DM_trainL4-8-16-32_MALAscore.npy

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README.md

@@ -50,13 +50,13 @@ concatenated along axis 0.
 ```bash
 pip install -U huggingface_hub
 
-# everything, 53 GB
+# everything, 52 GB
 hf download phi4-generative/phi4-critical-configs --repo-type dataset --local-dir phi4-critical-configs
 
 # 2D Monte Carlo ensembles only, 1.8 GB
 hf download phi4-generative/phi4-critical-configs --repo-type dataset --local-dir phi4-critical-configs --include "2D/*"
 
-# diffusion-model ensembles only, 2.4 GB
+# diffusion-model ensembles only, 0.7 GB
 hf download phi4-generative/phi4-critical-configs --repo-type dataset --local-dir phi4-critical-configs --include "DM/*"
 
 # a single file
@@ -134,40 +134,32 @@ which is the action at the top of this page without the field-independent consta
 ## Diffusion-model ensembles
 
 The folder DM/ holds the diffusion-model ensembles of the reference below at the same
-couplings, 2D at L = 128 and 3D at L = 64. Each configuration is an independent
-2000-step Euler–Maruyama integration of the reverse SDE of a score network, trained on
-Monte Carlo configurations either at the target L or only at smaller L. The two MALA
-files refine the kept configurations of the 3D ensemble trained at L ≤ 32 with 5000
-iterations of an exact Markov chain for e^{−S}, whose Metropolis-adjusted Langevin
-step uses either the learned score or −∇S as drift. The arrays are float32 with the
-same axis order as the Monte Carlo files.
+couplings. For each L a score network was trained on Monte Carlo configurations at that
+L. Each configuration is an independent reverse-SDE trajectory, integrated with 2000
+Euler–Maruyama steps on a logarithmic time grid in 2D and a linear time grid in 3D. The
+arrays are float32 with the same axis order as the Monte Carlo files.
 
 ```
-DM/2D/phi4_2D_L128_kappa0.2705_lambda0.022_DM_trainL128.npy                 (2048, 128, 128)      134 MB
-DM/2D/phi4_2D_L128_kappa0.2705_lambda0.022_DM_trainL8-16-32-64.npy          (2048, 128, 128)      134 MB
-DM/3D/phi4_3D_L64_kappa0.1923_lambda0.9_DM_trainL64.npy                     (512, 64, 64, 64)     537 MB
-DM/3D/phi4_3D_L64_kappa0.1923_lambda0.9_DM_trainL4-8-16-32.npy              (512, 64, 64, 64)     537 MB
-DM/3D/phi4_3D_L64_kappa0.1923_lambda0.9_DM_trainL4-8-16-32_MALAscore.npy    (509, 64, 64, 64)     534 MB
-DM/3D/phi4_3D_L64_kappa0.1923_lambda0.9_DM_trainL4-8-16-32_MALAexact.npy    (509, 64, 64, 64)     534 MB
+DM/2D/phi4_2D_L16_kappa0.2705_lambda0.022_DM.npy     (2048, 16, 16)          2 MB
+DM/2D/phi4_2D_L32_kappa0.2705_lambda0.022_DM.npy     (2048, 32, 32)          8 MB
+DM/2D/phi4_2D_L64_kappa0.2705_lambda0.022_DM.npy     (2048, 64, 64)         34 MB
+DM/2D/phi4_2D_L128_kappa0.2705_lambda0.022_DM.npy    (2048, 128, 128)      134 MB
+DM/3D/phi4_3D_L64_kappa0.1923_lambda0.9_DM.npy       (512, 64, 64, 64)     537 MB
 ```
 
-The file name gives the lattice sizes of the training data. The raw 3D outputs contain a
-few configurations outside the support of the Monte Carlo ensemble. As in the
-reference, the table below drops configurations with any site outside
-[−2.2576, 2.3138] or with |M| > 0.2297, the extremes of the L = 64 Monte Carlo
-ensemble of the reference. This removes 4 of 512 configurations from trainL64 and 3 of
-512 from trainL4-8-16-32. The MALA chains start from the 509 kept configurations of
-trainL4-8-16-32 and are not filtered. The observables are defined as above, and the
-Monte Carlo values at the same L are in the tables above.
+The raw 3D output contains a few configurations outside the support of the Monte Carlo
+ensemble. As in the reference, the table below drops configurations with any site
+outside [−2.2576, 2.3138] or with |M| > 0.2297, the extremes of the L = 64 Monte Carlo
+ensemble of the reference. This removes 4 of the 512 configurations. The observables are
+defined as above, and the Monte Carlo values at the same L are in the tables above.
 
-| ensemble | ⟨\|M\|⟩ | χ | U_L | ⟨φ_x²⟩ | ⟨S⟩/V |
-|---|---|---|---|---|---|
-| 2D trainL128 | 0.4387(46) | 762(17) | 0.4750(56) | 1.4148(12) | 0.39944(17) |
-| 2D trainL8-16-32-64 | 0.4623(41) | 673(17) | 0.5138(52) | 1.3960(14) | 0.40264(19) |
-| 3D trainL64 | 0.1229(21) | 674(38) | 0.5169(91) | 0.63820(39) | −0.09753(30) |
-| 3D trainL4-8-16-32 | 0.0942(22) | 738(36) | 0.402(16) | 0.62089(16) | −0.11130(17) |
-| 3D trainL4-8-16-32, MALA with learned score | 0.1258(21) | 680(35) | 0.5271(81) | 0.61929(11) | −0.11754(15) |
-| 3D trainL4-8-16-32, MALA with −∇S | 0.1156(18) | 643(33) | 0.509(10) | 0.617992(89) | −0.11650(13) |
+| D | L | ⟨\|M\|⟩ | χ | U_L | ⟨φ_x²⟩ | ⟨S⟩/V |
+|---|---|---|---|---|---|---|
+| 2 | 16 | 0.7935(72) | 24.69(74) | 0.5357(51) | 1.5010(79) | 0.3882(11) |
+| 2 | 32 | 0.6961(52) | 67.4(21) | 0.5587(36) | 1.4455(37) | 0.38954(50) |
+| 2 | 64 | 0.5979(49) | 207.5(66) | 0.5535(42) | 1.3698(29) | 0.39970(37) |
+| 2 | 128 | 0.4387(46) | 762(17) | 0.4750(56) | 1.4148(12) | 0.39944(17) |
+| 3 | 64 | 0.1229(21) | 674(38) | 0.5169(91) | 0.63820(39) | −0.09753(30) |
 
 ## Reference