5 Commits

Include renames

Author	SHA1	Message	Date
larsandClaude Opus 5	d858226294	Add configs/baseline.toml as the kept reference model ... A fixed comparison point for future architecture variants, so each experimental axis (routed trunk, WGAN generators, attention history, shared conditioning) is a single edit away from one known config. flow/flow autoregressive, hidden_dim 512 / 6 blocks per stage, physical conditioning, no router, 7.70M params. Chosen by ranking the five runs in analysis_runs/ by mean Jensen-Shannon divergence against the Geant4 reference: unrouted flow wins (0.172) over routed flow (0.197/0.200) and both WGAN runs (0.218/0.234), with the lead concentrated in per-event total deposited energy and the per-PDG marginals. batch_size 36864 is sized for one L40S on deepthought2 from a measured linear fit of this config's training step (reserved MiB = 0.9736 * bs + 115), giving ~36 GiB, 78% of the card. The comments record two measured facts that are easy to get wrong: WGAN is slower to *train* than flow (n_critic plus the gradient-penalty double-backward), its advantage being inference-only; and sample_secondaries_ar loops over all k_max slots unconditionally rather than short-circuiting on n_sec, which is what makes the autoregressive decoder the dominant cost on both axes. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>	2026-08-14 17:36:26 +02:00
larsandClaude Sonnet 5	a4c0443e01	Add bigger WGAN config (hidden_dim=512, n_blocks=6) ... Scaled-up variant of the already-trained wgan_h128_b4_physical.toml benchmark config, same training recipe (mode/epochs/lr/warmup), increased model capacity. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>	2026-07-30 17:08:46 +02:00
larsandClaude Sonnet 5	af2ee7c7ce	Add gumbel router configs sweeping learnable-knob combinations ... Extends the 10-expert EnergyRouter + physical-conditioning benchmark config with the new opt-in gumbel combine weights, isolating the learn_centers/learn_temperature axis: none, centers only, and centers+temperature. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>	2026-07-30 16:40:02 +02:00
larsandClaude Sonnet 5	db0f12be58	Add configs for router energy (embedding/physical) and WGAN baseline runs ... Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>	2026-07-27 15:53:04 +02:00
larsandClaude Sonnet 4.6	9277d79dff	Implement Phase 1: full data pipeline, model, training, and config support ... - Data pipeline: loader (parquet→numpy), transforms (log, local-frame Rodrigues rotation, Normalizer), StepsDataset with event-ID-based split - Model: SinusoidalEmbedding, ConditionEncoder, ResBlock, DenoisingMLP - Schedule: cosine DDPM and conditional flow matching loss (Lipman 2022) - Samplers: flow (Euler ODE), DDPM ancestral, DDIM deterministic - Training loop: AdamW + cosine LR, grad clipping, best-val checkpoint - Validation: per-dimension marginal summary (normalised space) - CLI: TOML config support with CLI-overrides; hyperparam-encoded output directory; config.toml with git hash saved into each run's checkpoint dir - 21 unit tests covering transforms, network, flow/DDPM losses, dataset splits Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-06-17 10:48:03 +02:00