AI-Assisted Simulation Debugger

Status: Early-stage concept - not yet a built system. This page documents the problem framing and proposed approach, not a completed implementation.

Vision

Engineering simulations often fail silently.

Numerical instability, invalid assumptions, parameter inconsistency, and mesh issues can propagate through complex systems in non-obvious ways.

This project explores whether AI systems can assist engineers in debugging simulation workflows.

Central Idea

Treat simulation failures not as isolated errors, but as:

propagating structural inconsistencies.

Classification of fault diagnostic methods

Proposed Workflow

graph TD
    A[Simulation Failure] --> B[Runtime Trace Analysis]
    B --> C[Constraint Extraction]
    C --> D[Structural Reasoning]
    D --> E[Probable Root Causes]

Why This Is Hard

Engineering simulations fail in non-obvious ways. A diverged solver, a violated physical constraint, or a misconfigured boundary condition all produce “wrong results” - but the failure signature is different in each case. Identifying root cause requires understanding the governing equations, the numerical scheme, and the coupling between subsystems simultaneously. Current debugging is manual and non-transferable.

Connection to Existing Work

The Codebase Indexer and Hybrid Code Analyzer explore the same underlying question for software: can structural analysis of a system accelerate root-cause diagnosis? The simulation debugger applies the same reasoning to physics models rather than code. → Codebase Indexer → Hybrid Code Analyzer