High-fidelity physics simulation, accelerated by orders of magnitude
Materials Engine builds physics-constrained simulation engines with machine learning inference so teams can run more high-fidelity iterations earlier in engineering design, process optimisation, and production workflows.
We replace trial and error and slow, expensive numerical simulation with fast, reusable physics-informed artificial intelligence—constrained by the governing physics, validated against trusted solvers, and designed to scale across real engineering programs.
What we do
1000x faster than numerical simulation
1000x more iterations in the same project
Greater model accuracy by embedding governing physics
Benefits of physics informed AI
Faster simulation unlocks deeper exploration: more concepts tested, more regimes covered, more iterations before prototypes—without sacrificing resolution or engineering rigour.
By accelerating key simulation components by 1,000×–1,000,000×, Materials Engine enables teams to evaluate orders of magnitude more configurations, reduce computational and experimental cost, reduce manufacturing failures, and shorten development cycles across machines, materials, and applications—while maintaining high physical fidelity.
Why it matters