Pareto Desk - notice

The app's agent prompt is derived from the agent skill "pymoo" (@k-dense-ai/pymoo) in the
repository k-dense-ai/scientific-agent-skills by K-Dense Inc.
  https://github.com/k-dense-ai/scientific-agent-skills (skills/pymoo)
The skill's front matter declares the Apache-2.0 licence. No text of the skill is redistributed
verbatim; the prompt was rewritten for this app.

The in-browser analysis (pareto.js) is an independent JavaScript implementation of definitions used
by the pymoo Python package (pymoo 0.6.2): fast non-dominated sorting with the vectorised dominator,
crowding distance ("cd"), PseudoWeights, the ASF decomposition, and constraint violation as the sum
of positive g values. No pymoo code is included. pymoo is Apache-2.0, https://github.com/anyoptimization/pymoo.
It was checked against pymoo 0.6.2 (hypervolume against moocore, which pymoo's HV indicator calls)
on 600 random problems: identical fronts, crowding distances and picks; hypervolume within 1e-13
relative.

If this skill contributed to a publication, K-Dense asks that it be cited:
  Kassis, T., Agarwal, V., He, Y., Patel, D., & Brueckner, A. M. (2026). Scientific Agent Skills:
  A Library of Procedural Knowledge for Research Agents. arXiv:2609.00065.

pymoo itself: J. Blank and K. Deb, "pymoo: Multi-Objective Optimization in Python," IEEE Access,
vol. 8, pp. 89497-89509, 2020.

The example data in example.js are illustrative. The beam designs are computed from the textbook
formulas for a rectangular steel cantilever (L = 1 m, 5 kN tip load, E = 200 GPa, 7850 kg/m3); the
van and heat-sink designs come from made-up models. None is measured data.