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RADIAL CORESCIENTIFIC
FUTURE-READY

AI Surrogates & Plasma Digital Twins

Leading fusion and semiconductor programmes now use AI surrogates to explore design spaces thousands of times faster than full simulations. We build physics-aware surrogates and digital twins on top of verified simulation data, so the AI is only as fast as it is right.

Sound familiar?

  • ●Each simulation takes hours or days, so design optimisation is impossible.
  • ●You want real-time prediction for control or a digital twin.
  • ●You need to infer plasma conditions from limited diagnostics (inverse problems).

What we offer

Surrogate models

Neural operators (FNO, DeepONet), Gaussian processes and reduced-order models trained on PIC/MHD data.

Physics-informed ML

PINNs and hybrid physics–ML models that respect conservation laws.

ML-accelerated simulation

Learned closures, field solvers and collision operators that speed up conventional codes.

Inverse design & Bayesian optimisation

Find the laser, target or reactor parameters that achieve your goal with minimal simulations.

Digital twin prototypes

Dashboards that couple surrogates with live or experimental data.

Frequently asked

How do you know the surrogate is trustworthy?+

We validate on held-out simulations, report uncertainty, and test physical constraints. Our V&V background applies to AI models too.

Do we need lots of data?+

Not necessarily. We generate targeted training data with active learning, so the expensive simulations are run only where they add information.

Related services

Have a simulation problem? Let’s scope it.

Tell us about your project. You will hear back from a founder within one working day, with a clear plan and a fixed quote.

Get a Quote