# Digital Twins A digital twin is a software replica of a physical system that updates in real time from sensor data. Not a static 3D model. A living simulation that mirrors what the actual equipment, process, or facility is doing right now. Two types matter: - **Physics-based twins** built from first-principles equations (thermodynamics, fluid dynamics, chemistry). Accurate but expensive to build and slow to run. - **Data-driven twins** built from historical sensor patterns. Fast but fragile: they break when conditions shift outside training data. The holy grail is hybrid: physics structure with data-driven calibration. Physics keeps the model grounded in reality. Data keeps it current. The hardest problem in digital twins is not building one. It's building the second one faster than the first. If every twin requires six months of custom engineering, you have a consulting business, not a product. See [[Deployment Velocity]]. Key players: Siemens (Simcenter), ANSYS, NVIDIA Omniverse, AspenTech (process industries), Azure Digital Twins. All approaching from different angles; none have solved the auto-generation problem. Related: [[Surrogate Models]], [[Simulation-Based Optimization]], [[Auto-Generated Physics Models]], [[Industrial AI MOC]] --- Tags: #deeptech #firstprinciple