Traditional simulation tools are designed to execute one study at a time. This research examines runtime architectures that enable multiple studies to run concurrently within a single program, allowing broader scenario exploration without repeated setup or execution cycles.
Executing multiple studies within a single runtime enables several practical capabilities:
Rather than treating each study as a separate event, analysis becomes an integrated process where insights emerge from comparison as much as from individual results.
As systems grow more complex, the number of plausible scenarios increases rapidly. Sequential execution struggles to keep pace with this growth, forcing teams to narrow exploration or accept longer decision cycles.
Architectures that support multi-study execution scale differently. They allow scenario breadth to increase without proportional increases in runtime overhead. This shifts simulation from a bottleneck into a catalyst for decision-making, enabling teams to reason about uncertainty more comprehensively and more efficiently.
Over time, this capability changes how simulation supports planning and operations. Instead of producing a single “best guess,” systems can evaluate ranges, sensitivities, and trade-offs within a unified analytical context. This leads to more informed decisions and greater confidence when operating under uncertainty.
Simulation studies are often used to explore uncertainty: varying assumptions, testing sensitivities, or comparing outcomes under different operating conditions. Despite this, most simulation platforms treat each study as an isolated execution. Running multiple scenarios typically requires restarting the simulation, duplicating configuration, or managing separate solver instances.
This workflow introduces friction. Each additional study compounds setup time, increases the likelihood of configuration drift, and slows the pace at which insights can be generated. More importantly, it limits how scenarios are compared. When studies are run sequentially, context is lost and relationships between outcomes are harder to evaluate.
As simulation use expands beyond single-point analysis toward broader decision support, this one-study-at-a-time model becomes a structural constraint. This research exists to address that constraint by rethinking how studies are executed at the runtime level.
A STRUCTURAL RETHINK OF STUDY EXECUTION
Most simulation systems assume that a study is the fundamental unit of execution. Each run initializes its own state, loads its own parameters, and proceeds independently from start to finish. This assumption simplifies implementation but constrains how efficiently scenarios can be explored.
The core insight of this research is that studies do not need to be isolated to be valid.
By designing runtimes that support multiple concurrent execution paths within a single program, studies can share common system state, physics models, and computational infrastructure while diverging only where assumptions differ. This enables parallel exploration without duplicating work or reinitializing the entire simulation environment.
Such architectures require careful management of state consistency, resource allocation, and numerical stability. However, when implemented correctly, they transform simulation from a linear workflow into a parallel exploration framework.