Predictive Digital Twin Computation: Forecasting Future Simulation States and Proactively Preparing Computational Outcomes

Digital twin systems increasingly support the continuous monitoring, simulation, prediction, and optimization of physical assets, including industrial machinery, manufacturing processes, buildings, transportation networks, energy infrastructures, and other complex cyber-physical systems. Their effectiveness frequently depends on the ability to execute computationally intensive simulations in response to changing physical conditions. However, many digital twin implementations still perform substantial simulation computation after new sensor observations, operational changes, or decision-making requirements have materialized. Under computationally expensive simulation models, high-dimensional physical systems, and rapidly changing operational conditions, this reactive execution paradigm may introduce considerable computational expenditure and delay the availability of simulation-based information. This paper introduces Predictive Digital Twin Computation (PDTC), a conceptual framework that investigates whether future physical-system states and their associated simulation requirements can be anticipated and computationally prepared before those states materialize. Rather than predicting only equipment failures or future operational measurements, PDTC attempts to identify multiple plausible future system states, construct corresponding simulation scenarios, and selectively execute their computational workloads in advance. Its architecture integrates future-state forecasting, probabilistic scenario generation, simulation workload estimation, predictive computational preparation, state-compatible result reuse, incremental simulation continuation, simulation validity assessment, and adaptive resource scheduling. For example, a manufacturing digital twin may anticipate several possible future production-line load conditions and proactively simulate their corresponding temperature distributions, energy consumption, equipment loads, production performance, and operational risks. When the actual physical system subsequently approaches one of the anticipated conditions, the digital twin may retrieve a compatible prepared simulation result or continue computation from an appropriately validated intermediate simulation state. A unified optimization formulation is developed to evaluate the expected benefits of predictive simulation preparation while accounting for state prediction uncertainty, simulation execution costs, computational preparation expenditure, result compatibility, simulation fidelity, information freshness, storage requirements, and real-time operational constraints. The framework further distinguishes between complete simulation-result reuse, partial simulation-state reuse, and predictive scenario preparation, recognizing that similarity between physical states does not automatically establish the validity of previously generated simulation outcomes. An experimental methodology is proposed to evaluate simulation response time, total computational expenditure, prediction accuracy, scenario coverage, result validity, resource utilization, and decision-support performance. The proposed framework does not assume that advance simulation is universally beneficial; its effectiveness depends on the predictability of physical-system evolution, the computational cost of simulation, the probability of future state realization, and the validity of prepared computational outcomes. By shifting selected simulation workloads from the moment when physical conditions change to earlier periods when computational resources are available, PDTC investigates a predictive computational paradigm for digital twins in which future simulation requirements are anticipated and selectively prepared before they become operationally necessary.