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Research: Continuous marketing hydrodynamics and Scientific Machine Learning foundations - #9
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…ning foundations - Add formal research monograph on differentiable hydrodynamics, ergodic measure preservation, and multiscale downscaling (paper/sciml_marketing_hydrodynamics.md). - Record OOTB-032 frontier speculation in OUT_OF_THE_BOX.md for continuous marketing fields. - Record dialectical debate and falsifiable hypotheses in DEBATES.md. - Register canonical SciML and optimal transport literature in paper/references.bib.
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Summary
Formalizes Scientific Machine Learning (SciML) and continuous hydrodynamic marketing field foundations for the FunnelScript research program, cross-pollinating theoretical mechanisms from Google Research's
swirl-dynamics.Key Deliverables
paper/sciml_marketing_hydrodynamics.md): Complete formal treatise on continuous density fieldsScanOdeSolveradjoint sensitivity, and WENO-NN shock capturing.DEBATES.md): Records the tri-scale trade-off between Macro Bayesian MMM, Micro Discrete-Event ABM, and Meso Continuum Marketing Hydrodynamics, establishing 4 falsifiable research hypotheses (OUT_OF_THE_BOX.md): Records entry OOTB-032 detailing theoretical mapping from fluid mechanics to marketing engineering.paper/references.bib): Adds 10 peer-reviewed academic references (ICML, ICLR, NeurIPS, Nature Machine Intelligence, JCP).Verification
uv run --no-project --python 3.12 --with pytest pytest -v(63 passed, 154 subtests passed).tests/test_docs_links.pypassed.tests/test_accepted_decision_index.pypassed.git diff --cached --checkclean.python3 agents/skills/anti-slop-engineering/scripts/diff_guard.pyclean.