We build machine‑learning surrogate models and physics‑informed networks that approximate high‑fidelity simulations with near real‑time latency enabling rapid design exploration, optimisation and control without giving up the underlying physics.
PHYSICS INFORMED AI
PINNs, neural operators and reduced order models turn batches of slow runs into fast, callable models that can sit inside apps, optimisation loops, controllers and digital twins.
CORE CAPABILITIES
Pragmatic use of AI: surrogates that sit alongside your existing solvers and workflows, not black box replacements.
PATTERNS
Most projects fall into a few recurring patterns — each with its own sweet spot and limitations.
Neural networks trained on simulation fields to predict pressure, temperature or stress distributions from boundary conditions and key parameters.
Fast models that map design inputs to scalar outputs such as lift, drag, peak stress or mixing indices — ideal for optimisation loops.
Surrogates constrained by governing equations or reduced order bases, improving robustness when data is sparse or noisy.
HOW WE WORK
Most projects fall into a few recurring patterns each with its own sweet spot and limitations.
Identify simulations that are run often and take too long: parametric studies, DOEs, UQ or control oriented models.
Gather representative simulation runs and, where useful, encode governing equations or reduced order structures.
Train surrogate models, compare against high fidelity results and define the domain where predictions are trusted.
Wrap models into apps, optimisation loops, controllers or digital twins, with monitoring and update plans.
We usually start with a single high‑impact bottleneck and clear success measures before rolling AI out more broadly.
