Physics verification for AI-generated hardware designs

Raising $10,000active

Builds a physics-based verifier for AI-generated hardware that flags unverifiable aspects as UNCHECKED, enabling a propose-and-check loop to test and improve parts and assemblies for strength and fit.

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Project Details

AI can design real hardware quickly, but it cannot verify if the design will hold up. I created a tool to check this. It takes a hardware design, tests it against real physics, and marks anything it cannot verify as UNCHECKED instead of claiming it is safe. For example, consider a wheelchair seat bracket intended for a 100 kg occupant on rough ground. The design suggested by a chatbot shows a safety factor between 0.31 and 0.91, indicating it would break under the weight. My checker identifies a design that withstands a factor of 2.90. It confirms that design while including the mounting bolt holes. The file it exports is exactly the solid that got verified. The checker operates on a laptop without a GPU, and all the calculations come from open code, allowing me to reproduce any of it live.

The checker works beyond just single parts. It matches textbook physics to about one percent accuracy. It successfully checks a 44-piece engine assembly with no interference during a full rotation. It also verified a wearable ultrasound gimbal while refusing to certify five aspects it could not check, such as skin pressure and thermal rise. This refusal is a feature, not a flaw. The UNCHECKED flag is a structural part. A claim that hasn’t been measured cannot be considered passed. This loop isn’t just theoretical. The 44-piece engine was produced by a model within it, using a rented computing cluster. It proposed and revised designs against the checker until there was no interference. I built the wheelchair, gimbal, and hook by hand to test the checker. The engine illustrates what the loop can achieve with a driving model. However, I haven't made it affordable yet or assessed how reliably it works on designs it has never seen, and that’s the purpose of this funding.

The aim is to create a loop where a model proposes a design and the checker tests it with real physics. This will expand from a single part to a complete machine, making it cheap to verify that a design will hold. The checker doesn't require a GPU; the driving model does. That's why the engine needed a cluster. The funding request is for $10,000 to measure the reliability of the loop on new designs and up to $100,000 for a larger model and a hosted version that others can use. Essentially, it is a scalable oversight plan. A proposer cannot submit a claim unless its checker has confirmed it independently, in one of the few areas where the checker is validated against textbook physics rather than another model's viewpoint.

Theory of Impact

Most AI oversight faces the same problem: checking the checker is costly. To know if an AI's answer is correct, you typically need another model to evaluate its plausibility or a human to verify the information. Both options are expensive and not entirely reliable. Physical engineering is one of the few areas where this issue doesn't exist. A solver checks a design against established physics principles, so the verifier is trustworthy. It doesn't rely on a model's assurance; instead, it agrees with mathematical truths that have known answers.

This creates a tested for the framework that scalable oversight relies on: a proposer cannot present a claim that its checker hasn't independently verified. The failure it seeks to prevent involves an AI making assertions that seem correct yet are wrong, especially when users cannot double-check the information. This mirrors failures that can have disastrous consequences when the stakes are higher than a simple decision.

A model has already turned this process into a functioning engine, proving the mechanism works. The key question, and reason to invest in this, is what happens when optimization pressure is applied. When trained against the checker, does a model produce designs that genuinely work, or does it find loopholes in what the checker fails to assess? This is known as specification gaming. This scenario provides a unique opportunity to observe it inexpensively, with quick and reliable verification, before similar patterns are trusted in contexts where a false "verified" status cannot be reversed.

People

Peter Boctor

Team Member

Grants Received– no grants recorded

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Funding Asks

grantmaking.ai Launch Round

Applied

Minimum

$10,000

Ideal

$100,000

How the money will be spent

The checker runs on a laptop, but the model that drives the loop doesn’t. This is what the funding covers. The loop already works with a real model and produced the 44-piece engine on a rented cluster. However, it has operated only on rented resources, and it has never been cheap. I have not tracked how often it succeeds with designs it has not seen before. The test for that is ready. It includes eleven held-out design families, checked to ensure a maximum overlap of 0.14 against a 0.50 threshold. No one has run it yet. Producing that pass rate is what the first GPU hours will pay for.

The request is staged. The $10,000 minimum rents GPU hours to complete the held-out sweep and report the pass rate here, whether it is good or bad. The $25,000 option buys a workstation with an RTX PRO 6000. This allows for the many small iterations the development loop requires, using electricity instead of rental money. Metering often slows down real progress in design. I handle the power, storage, and electrical work myself. The $100,000 option funds the capacity for the cluster-scale model the engine needed, which will be owned instead of rented. It also includes hosting so a workshop or another lab can access the checker through a browser. Renting that node costs about $140,000 a year, but this buys it outright.

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