Predictive Scientific Computation: Anticipating Future Simulation Requirements and Precomputing Probable Computational Outcomes

Scientific computing increasingly relies on computationally intensive simulations to investigate complex physical systems, evaluate engineering designs, explore material properties, model molecular behavior, and analyze environmental processes. Many scientific investigations involve iterative parameter exploration, repeated simulation execution, and progressive refinement of computational conditions. Researchers frequently modify a limited number of parameters while retaining substantial portions of the underlying simulation configuration, numerical methodology, initial conditions, or computational structure. Nevertheless, conventional scientific computing infrastructures generally initiate simulation tasks only after researchers explicitly submit them. This reactive execution paradigm may result in repeated computational expenditure, underutilized GPU resources during certain periods, and unnecessary delays when subsequent simulation requirements could potentially have been anticipated. This paper introduces Predictive Scientific Computation (PSC), a conceptual framework that investigates whether future scientific simulation requirements can be anticipated from researchers' experimental progress, historical parameter selections, simulation trajectories, scientific objectives, and evolving parameter-space exploration patterns. Rather than executing every simulation only after an explicit computational request, PSC estimates which simulation configurations are likely to be required next and selectively prepares their computational outcomes in advance. Its architecture integrates scientific workflow monitoring, parameter-space forecasting, simulation task prediction, predictive computational scheduling, intermediate simulation-state preparation, numerical validity verification, and adaptive result reuse. A central characteristic of the proposed framework is its ability to distinguish between predicting future simulation configurations and predicting reusable computational dependencies. For example, when a researcher investigates material properties at different temperatures, PSC may anticipate that subsequent simulations will involve 300 K, 310 K, and 320 K. It may selectively execute these simulations during periods of relatively low GPU utilization, favorable electricity prices, or lower-cost cloud computing availability. More importantly, it may identify shared computational structures and prepare reusable intermediate results that reduce the computational expenditure associated with subsequent parameter variations. The framework therefore extends conventional result caching by investigating the proactive execution of future scientific workloads and the preparation of reusable numerical computation states. A unified optimization formulation is developed to evaluate the expected computational benefits of advance simulation, accounting for prediction uncertainty, simulation costs, parameter-space relationships, numerical approximation errors, resource availability, storage expenditure, and result validity. Particular attention is given to the distinction between exact result reuse, conditionally reusable intermediate states, approximate simulation reuse, and surrogate-assisted computation. An experimental methodology is established to evaluate simulation prediction accuracy, computational savings, GPU utilization, parameter-space coverage, numerical accuracy, result reuse rates, and overall execution efficiency. The framework does not assume that predictive simulation is universally advantageous; its effectiveness depends on the predictability of scientific workflows, computational dependencies, numerical compatibility, and the economic value of preparing future outcomes. By extending predictive computing from generative AI inference to scientific simulation infrastructures, this research investigates a broader computational paradigm in which scientific systems anticipate future experimental requirements and selectively execute relevant numerical computations before researchers explicitly request them. Its practical effectiveness remains subject to empirical validation.