Make the virtual decision before the physical change.
LargeQuant connects digital twins, calibration, CFD/FEA, HPC, surrogate optimisation, uncertainty and reliability to evaluate design or operating changes before physical execution.
Industrial optimisation must respect physics, constraints and uncertainty.
A predicted optimum is not automatically an acceptable operating point. Industrial systems have equipment limits, quality requirements, model discrepancy, measurement error and changing operating conditions. LargeQuant combines optimisation with the evidence needed to decide whether a candidate is credible enough to progress.
Combine the engineered asset with the environment and the reliability decision.
Stratomission's digital-twin visual model makes the physical system tangible. LargeQuant connects that view to operating conditions, uncertainty, reliability and evidence-backed decisions.
Explore Stratomission →High-value quantitative workflows.
Digital-twin calibration
Fit model parameters to accepted observations and preserve residual/error evidence.
Design-space exploration
Evaluate many candidate designs using a mix of surrogate and high-fidelity evidence.
CFD / FEA workflows
Run trusted solver cases and extract objective evidence through bounded adapters.
Process optimisation
Search operating variables under quality, physical and economic constraints.
HPC orchestration
Submit approved engineering cases to Slurm and reconcile jobs/results.
Reliability-constrained autonomy
Allow autonomous progression only when deterministic and probabilistic criteria are satisfied.
Connect the domain model to the evidence and decision layer.
Quantitative infrastructure around your domain expertise.
Digital twins
Physics-system models with observation-based calibration.
CFD / FEA
Trusted OpenFOAM and CalculiX boundaries when runtimes are available.
HPC
Controlled Slurm submission and reconciliation.
Surrogate optimisation
Gaussian-process and multi-fidelity campaigns.
Uncertainty budgets
Decompose predictive, measurement, discrepancy and operational sources.
Reliability gates
Probability-based quantitative criteria before automated progression.
Designed to connect to the stack you already operate.
- Plant historian or test data
- CAD/mesh-derived solver cases
- OpenFOAM / CalculiX environments
- Slurm/HPC clusters
- Process control or engineering parameter sets
- Quality / throughput / energy objectives
Built for cross-functional quantitative work.
- Simulation & CAE
- Process Engineering
- Industrial R&D
- Digital Twin Teams
- HPC / Scientific Computing
- Operations Excellence
Keep the basis of the decision inspectable.
Every promoted operating point can be tied back to the model, calibration state, solver or surrogate evidence, uncertainty assumptions, deterministic constraints and recorded reliability criterion used at decision time.
Explore evidence & provenance →