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Scientific Computing

Use Sounio for data analysis with built-in uncertainty quantification

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Scientific Computing with Sounio

Sounio is designed for scientific applications where measurement uncertainty matters.

Example: Data Analysis Pipeline

fn analyze_samples(samples: Vec<Knowledge<mg>>) -> Knowledge<mg> with IO {
    // Calculate mean with uncertainty propagation
    let mean = samples.iter().mean()

    // Confidence gate: only proceed if uncertainty is acceptable
    if mean.confidence < 0.90 {
        perform IO::warn("Low confidence result")
    }

    mean
}

Features for Scientists

  • GUM-compliant uncertainty propagation
  • Provenance tracking for reproducibility
  • Unit type safety prevents dimensional errors
  • Effect system makes side effects explicit

Real-world Applications

  • Climate modeling
  • Medical research
  • Particle physics
  • Materials science

Sounio now includes a COVID 2020 kernel fixture that demonstrates temporal-validity typing and confidence-aware compilation:

souc check tests/run-pass/covid_2020_kernel.sio

Why this matters:

  • policy-relevant models carry explicit validity windows
  • uncertainty constraints are enforced at compile time, not after deployment
  • provenance is preserved so assumptions remain auditable