Commit Graph

2 Commits

Author SHA1 Message Date
Frank14f
5f061bec06 feat(train): cross-Re transfer pipeline — re60/re200/re400 calibrations + script
- Add crossre_transfer.sh: calibrate → transfer-train for re60→re200→re400
- Add re60 config (ν=0.006667, SI=800, uniform+free-slip, very weak shedding)
- Calibrate re60, re200, re400: FORCE_SCALE, SENS_SCALE, dtw_norm_scale, SIM_BP
- Fix all paths: use DynamisLab submodule CelerisLab, remove external ~/CelerisLab
- Remove _clean_cache() from envs/calibrate — CelerisLab handles internally
- Move V4 backups to old/: env_karman_2000x600, train_karman_2000x600, etc.
- train_karman.py: save model + vecnormalize every episode (non-optional)
- Update TRAIN_KNOWLEDGE.md: file structure, calibration table, cross-re guide
- All 3 Re verified: 5-episode transfer test passed (re60: 0.64, re200: 0.43, re400: 0.49)

Co-authored-by: Cursor <cursoragent@cursor.com>
2026-07-03 00:21:49 +08:00
Frank14f
b3ee72e144 feat(train): V5 parameterized training pipeline — Karman + Illusion verified
Calibration-driven, no_bias only, 2000x600 grid. All cases share unified
env/train/calibrate pattern. Multi-GPU server deployment ready.

Core additions:
- calibrate.py: Phase 0 calibration (karman/illusion), produces
  calibration.json with rounded FORCE_SCALE, SENS_SCALE, SIM_BP/VAL
- env_karman.py: parameterized Karman cloak env (calibration + config_path)
- env_illusion.py: illusion env with FFT harmonics target (S_DIM=14)
- env_vortex.py: vortex cloaking env (lamb/taylor, MAX_STEPS=150)
- train_karman.py, train_illusion.py: parameterized training scripts
- launch_multi.sh: sequential multi-GPU launcher (7-min staggered)
- SERVER_DEPLOY.md: complete server setup, calibration, training guide
- calibrations/re100/ & calibrations/illusion_1L/: pre-run calibrations

Fixes:
- SIM_VAL[-1] 0.95 -> 1.0 (r_sim maps to full [0,1] range)
- Cross-Re configs: re50/200/400 (viscosity-only variants)

Verified end-to-end on GPU0+GPU1:
- Karman V5 20-ep: best reward 0.459 at Ep16 (monotonic rise)
- Illusion 20-ep: best reward 0.224 at Ep19 (harmonics, DTW learning)

Co-authored-by: Cursor <cursoragent@cursor.com>
2026-07-01 20:10:27 +08:00