AI for Engineering Knowledge Management
Harvard & MIT research shows LLMs can predict planetary orbits but fail to understand Newton’s laws.
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3 min read
Dr. Maor Farid
Maor Farid is the Co-Founder and CEO of Leo AI, the first AI platform purpose-built for mechanical engineers. He holds a PhD in Mechanical Engineering and completed postdoctoral research at MIT as a Fulbright fellow. A Forbes 30 Under 30 honoree and former AI researcher and Mechanical Engineer in an elite military intelligence, Maor leads Leo AI's mission to transform how engineering teams design better products faster.
BOTTOM LINE
LLMs can fit curves to data but they cannot derive the physics behind them. For mechanical engineers, that distinction is not academic — it determines whether an AI-assisted analysis can be trusted to inform a real design decision.
Engineering AI that understands physical laws, not just patterns in text, is the foundation for tools engineers can actually rely on.
The impact is quantifiable. Teams using Leo report 8.3+ hours saved per engineer per week. Design errors drop by 34%. And organizations see 211% faster time-to-market when their entire team has access to the same knowledge base instead of working in silos.
Harvard & MIT research shows LLMs can predict planetary orbits but fail to understand Newton’s laws. Discover how Physically Informed Neural Networks (PINNs) and Leo AI’s Large Mechanical Model (LMM) bring true physics understanding to mechanical engineering.
In a recent study from Harvard and MIT, researchers demonstrated a fascinating limitation of today’s large language models (LLMs): while they can predict planetary orbits with impressive accuracy, they fail to understand the underlying physical laws driving those motions.
The experiment involved asking LLMs to predict both:
The motion (orbit, trajectory) of a planet near a central attracting object.
The attracting force (vector field) acting on that planet.
The results were surprising yet revealing. The orbit prediction was almost perfect - visually indistinguishable from the ground truth. But the predicted force field? Completely wrong.
Kepler-Level Predictions, Newton-Level Failure
This finding highlights a key point: LLMs can describe what happens (similar to Kepler’s laws of planetary motion) but cannot explain why it happens (Newton’s second law: F = ma).
In other words:
Prediction ≠ Understanding.
For engineers, this limitation is not a shock. LLMs are language models, designed to generate the most probable sequence of words. They are not inherently equipped to reason with physical laws - a core skill in engineering, physics, and mechanical system design.
IN PRACTICE
What Engineers Are Saying
"It handles complex mechanical calculations — stress, thermal, fluid — and often shares the Python-based logic behind the result, which makes it easier to verify and include in technical reports. We see 96% accuracy on technical queries."
— Dorian G., AI Engineer
Why This Matters for Mechanical Engineers
In mechanical engineering, understanding why a system behaves a certain way is crucial. Predicting stress on a beam, motion in a gear train, or airflow over a wing is not enough - the engineer must understand the physics to design safely and efficiently.
Relying solely on generic AI models for engineering decisions could lead to designs that look correct in simulation but fail in real-world application.
The Path Forward: Physics-Aware AI Models
The good news? Researchers and engineers are already developing physics-informed AI that can reason within the laws of mechanics.
Two promising approaches:
PINNs (Physically Informed Neural Networks): These integrate physical equations directly into the AI model’s training, ensuring outputs obey known laws of motion, thermodynamics, or material science.
Leo AI’s Large Mechanical Model (LMM): Designed specifically for mechanical engineers, Leo AI’s LMM understands the relationships between mechanical parts, assemblies, and physical constraints. It combines engineering datasets with embedded physics rules, enabling accurate predictions and explanations for mechanical systems.
How Leo AI Bridges the Gap
Unlike generic LLMs, Leo AI’s LMM is tailor-made for engineering. It can:
Interpret CAD and PLM data while respecting physical constraints.
Suggest design optimizations that follow Newtonian and other engineering laws.
Assist in troubleshooting mechanical systems with both predictive and explanatory insight.
This makes Leo AI not just a search or automation tool - but a physics-aware engineering assistant.
FAQ
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