Simulation Lookup

Find the nearest previously simulated cases before running a new simulation.

Industry	Level	Difficulty	Recommended mode
Mechanical engineering	5 of 5	Frontier	flat

Coming soon

A complete, runnable Simulation Lookup reference implementation is in progress. The architecture and core code below already work with SrvDB 0.3.

The problem

• Simulations are expensive.
• Past runs are rarely reused.
• Parameter spaces are large.

How SrvDB powers Simulation Lookup

1. Simulation inputs and result summaries are stored as vectors.
2. New parameter sets retrieve the nearest past runs.
3. Engineers reuse or narrow the search.
4. Rust API in simulation pipelines.

What changes

Today	With Simulation Lookup on SrvDB
Re-running similar cases	Reusing nearest results
Lost runs	Searchable history
Blind exploration	Guided by neighbours

How it works

1. Vectorise inputs (at least 128 values).
2. Store with result summaries.
3. Query new cases.
4. Decide to reuse or run.
5. Add new runs.

SrvDB features it uses

• Exact search (flat): every query compares against every stored vector, so results are exact and explainable.
• Rust API: embed SrvDB directly in Rust services and devices.
• Crash-safe persist(): after it returns, data survives a crash or power loss and reopens exactly as committed.
• upsert: inserts new items and replaces changed ones, so the knowledge base stays current.

Use cases

• FEA reuse
• CFD case lookup
• Thermal studies
• Crash simulations
• Design-of-experiments
• Surrogate-model seeding

Core code

embed() is your embedding model (for example sentence-transformers, or a function that turns your records into feature vectors).

import json
import srvdb

db = srvdb.Database("./simulation-lookup-db", dimension=128)

def ingest(items):
    """items: list of (id, text, metadata dict)"""
    db.upsert(
        ids=[i for i, _, _ in items],
        embeddings=[embed(t) for _, t, _ in items],
        metadatas=[json.dumps(m) for _, _, m in items],
    )
    db.persist()                      # crash-safe checkpoint

def nearest_runs(parameters, k=5):
    hits = db.search(embed(parameters), k)
    return [(json.loads(db.get(i)), round(score, 3)) for i, score in hits]

Limits and honest notes

• Neighbours are guidance, not validated results.

Build roadmap

1. Prototype: load a sample of real data with flat mode and check that searches return what an expert expects.
2. Measure: if you move to an approximate mode, compare its results with flat on the same queries (recall).
3. Pipeline: add continuous upsert and decide when to call persist().
4. Harden: test restarts and crashes, add logging, review every output path with domain experts.
5. Deploy: ship the database directory with the application or service.

All Mechanical engineering projects

Level	Project	What it does
1 · Beginner	Part Finder	Find existing parts by describing what they do.
2 · Intermediate	FMEA Assistant	Drafts and reviews FMEAs grounded in past FMEAs and failures.
3 · Advanced	Vibration Signature Matcher	Matches vibration signatures to known fault patterns.
4 · Expert	Geometric Design Search	Search CAD models by shape using 3D shape embeddings.
5 · Frontier	Simulation Lookup (this page)	Find the nearest previously simulated cases before running a new simulation.

See also: Use cases · Live examples (coming soon)