The OpSimTech Physics-AI Stack.
A three-layer architecture. The ground truth is physics. The acceleration layer is AI. The output is a digital twin engineers can actually use.
High-Fidelity Physics Core
Where governing equations are solved at full fidelity. Run offline to build understanding, validate the AI layer, and generate training data. Industrial-grade meshing, validated turbulence models, full conservation laws.
Computational Fluid Dynamics
Powering aircraft & turbomachinery design, ship hydrodynamics, HVAC, and process equipment — wherever fluid behavior drives industrial outcomes.
Smoothed Particle Hydrodynamics
Mesh-free particle methods for the hardest geometries — complex moving boundaries, free-surface flows, slurry & multiphase mixing where mesh-based solvers break down.
Discrete Element Method
Granular mechanics & particle-level manufacturing.
Physics-AI Hybrid Layer
Built to run alongside your existing CFD workflow — rapid surrogate sweeps replace the slow iteration loop, while your validated CFD results anchor the model's physical ground truth. Three branches, matched to the physics and the data.
Physics-Guided Surrogates & ROMs
The workhorse. ROMs, Gaussian processes, ML with physics constraints embedded in architecture or loss — tractable, fast, and validated against your CFD.
Most deployments · data existsPhysics-Informed Neural Networks
The specialist tool. Governing equations encoded directly in the loss — used for data-sparse, physically constrained domains where direct PDE enforcement adds unique value.
Data-sparse · constrained domainsData-Driven + Physics Validation
Sensor-rich systems where high-frequency data dominates. CFD/SPH used as offline validators, not real-time solvers.
When sensor data is abundantReal-Time Digital Twin
A continuously updated model fed by live sensor data, scored against multifidelity state estimates, exposed through APIs and dashboards. Designers and operators query a working twin — not a static report.
Live Sensor Fusion
Stream sensor signals into the twin state in real time — Kalman, Bayesian, or ML-based filters.
State Estimation
Blend low- and high-fidelity models. Speed where you can; accuracy where you must.
Decision Support
Optimization, scenario sweeps, regulatory compliance views — direct outputs of the twin.
