Reproducible numerical gym for physics-informed lattice metamaterial design
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Updated
Jun 1, 2026 - Python
Reproducible numerical gym for physics-informed lattice metamaterial design
Local-first MLOps lifecycle with Airflow, MLflow registry, FastAPI serving, Prometheus, and reproducible three-run evidence.
Reproducibility bundle for 'Online Routing for Next-Action Prediction' (Langiu & Pilato): online routing over a classical + neural predictor pool; leave-one-user-out benchmark. DOI 10.5281/zenodo.20995956
Reproducible benchmark harness that builds the same deterministic sample content across static site generators (Astro, Eleventy, Hugo, Jekyll) and reports cold build time, warm rebuild time, and output size (raw + gzipped). Detects installed toolchains and honestly skips the rest — never fakes results. Zero dependencies, Node 20+.
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