# Auto-Generated Physics Models The holy grail of industrial AI: automatically building first-principles physics, chemistry, or thermodynamics models from facility data without manual engineering. Why it matters: building a physics-based [[Digital Twins]] for a single process unit currently takes weeks to months of domain expert time. If you can auto-generate these models from sensor data + a [[Knowledge Graphs for Industrial Data]], deployment drops from months to days. That's the difference between a consultancy and a platform. See [[Deployment Velocity]]. Why it's hard: physics model auto-generation requires mapping raw sensor relationships to governing equations (mass balance, energy balance, reaction kinetics). The system needs to know that sensor A measures temperature at the inlet of a heat exchanger, that the heat exchanger follows Fourier's law, and that the outlet temperature depends on flow rate, inlet temperature, and fouling state. Inferring this chain automatically from data is an unsolved problem. Current state: this is the most ambitious and least evidenced claim in the industrial AI space. Companies claim it. Nobody has publicly demonstrated it at production scale. Equipment model libraries (pre-built physics templates for common equipment) are a partial solution, but you still need domain expertise to configure and validate them. The investment lens: any company claiming auto-generated physics models should be able to show one specific model that was auto-generated and then validated against plant data. If they can't, the claim is aspirational. Related: [[Digital Twins]], [[Surrogate Models]], [[Deployment Velocity]], [[Industrial AI MOC]] --- Tags: #deeptech