AI & Functional Safety: Simulation and Synthetic Data — Validity Arguments for Virtual Testing

Date
2026-12-22
Location
Online
Host
Zach L

About this event

A live 30-minute expert session on Simulation and Synthetic Data — Validity Arguments for Virtual Testing (AI / ML — AI & Machine Learning Safety). What We'll Cover: Where simulation and synthetic data fit in the ML safety lifecycle and what claims they can and cannot support Step by step: characterize the domain gap, calibrate sensor models, validate against matched real-world samples, bound transfer error How simulation credit maps to tool-confidence arguments and the correlation report artifacts produced Common mistakes: training and validating in the same simulator, hiding a shared rendering bias The sim-versus-field correlation evidence and validity envelope statement an assessor requires Related topics: simulation validity · synthetic data safety · sim to real gap · domain gap · sens

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About Functional Safety Foundations

This online session delivers expert-led instruction on the regulatory landscape and implementation approach for Functional Safety Foundations.

The cross-sector fundamentals underlying every functional safety standard: risk as severity × likelihood, the safety function concept, integrity levels, the safety lifecycle, and the split between random and systematic failures.

What it regulates

Shared vocabulary and reasoning: hazard → risk → risk reduction allocation → safety requirement → verified implementation → maintained integrity in operation.

Who must comply

Everyone entering the field — the conceptual base of the entire catalog.

Key requirements of Functional Safety Foundations

  • Hazard and risk fundamentals
  • Safety function anatomy: sensor-logic-actuator with defined safe state
  • Integrity ladders (SIL/PL/ASIL/DAL) as risk-reduction currencies
  • Random vs systematic failure treatment
  • Lifecycle and management of functional safety incl. competence

Key concepts: Functional Safety Foundations

Functional safety management
Planning, competence, documentation and assessment wrapped around technical work.
Random vs systematic
Hardware wear-out/chance failures quantified statistically versus design/process errors controlled by rigor.
Risk reduction allocation
Distributing required risk reduction among mechanical design, safeguards, control functions and procedures.
Safe state
The defined condition a function drives the equipment to on demand or fault.

Frequently asked questions: Functional Safety Foundations

Why can't testing alone prove safety?

Rare-event targets (like 1e-7/h) are unverifiable by test duration, and systematic errors evade random testing — hence the standards' dual machinery of quantified hardware analysis plus process rigor and independent assessment.

Standard information based on the published text of Functional Safety Foundations. Event content is provided by the host. For authoritative guidance, consult the official standard body.