neqsim-uncertainty-quantification

name	neqsim-uncertainty-quantification
calculation_basis	advisory
version	0.1.0
description	Monte Carlo uncertainty quantification, tornado sensitivity and global sensitivity analysis for NeqSim tasks. Supplies inverse-CDF marginals (uniform, triangular, normal, log-normal fitted from a stated P10/P90, deterministic), unit-hypercube samplers (seeded pseudo-random, Latin hypercube, Halton), a technical/economic model split that caches the expensive flowsheet stage so price and cost parameters never trigger a re-solve, linear-interpolation percentiles in the ascending p10<=p50<=p90 convention the task gate enforces, a scale-invariant split-half convergence check, a swing-ranked one-at-a-time tornado, optional SALib Saltelli/Sobol' and Morris backends that expose the interaction effects a tornado is blind to, an optional chaospy polynomial-chaos surrogate that reaches the same statistics in one to two orders of magnitude fewer model evaluations, and emission of the uncertainty block that the task report generator and CI gate consume. USE WHEN: a task must report P10/P50/P90, a tornado diagram or a probability of a negative outcome, a Monte Carlo loop wraps an expensive NeqSim simulation, parameters must be ranked before a sensitivity budget is committed, an interaction between uncertain inputs is suspected, or an existing uncertainty block must be audited for sample count, convergence and percentile convention.
last_verified	2026-08-08
requires	{"python_packages":[],"java_packages":[],"env":[],"network":[]}

Uncertainty Quantification

A Standard or Comprehensive NeqSim task must report P10/P50/P90 and a tornado diagram. In practice that gets written from scratch in every notebook, and the same four defects recur: sampling that clusters because it is plain pseudo-random at n = 200, a Monte Carlo loop that re-solves the flowsheet for a gas price that never touches it, no check that the run converged, and a tornado presented as if it were a sensitivity analysis when it cannot see interaction.

This skill supplies the sampling, the statistics, the caching, the convergence gate and the report block. It does not own the model — the task supplies that.

When to Use

• A task must produce uncertainty for results.json: P10/P50/P90, mean, standard deviation, probability of a negative outcome, tornado.
• A Monte Carlo loop wraps an expensive NeqSim flowsheet and the evaluation budget is the binding constraint.
• Parameters must be screened before a sensitivity budget is committed.
• An interaction between uncertain inputs is suspected and a tornado is not enough.
• An existing uncertainty block must be audited: enough samples, converged, correct percentile convention?

When Not to Use

• As an optimiser. Searching for the best setpoints is neqsim-optimization-and-doe; this skill propagates uncertainty through a fixed design.
• To invent input ranges. A distribution with no basis produces a precise answer to an arbitrary question — record where each range came from.
• For correlated inputs. Every marginal is sampled independently; correlation between, say, price and cost inflation is not represented.
• As a substitute for a risk register. A probability distribution on an output is not a hazard assessment.

Inputs

Input	Meaning
parameters	list of Distribution objects, each with name, unit, kind
kind	"technical" (drives the expensive stage) or "economic" (cheap stage only)
model	f(values) -> float, for a single-stage study
technical / economic	the two-stage split: ,