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Medical simulation, or more broadly, healthcare simulation, is a branch of simulation related to education and training in medical fields of various industries. Simulations can be held in the classroom, in situational environments, or in spaces built specifically for simulation practice. [ 1 ]
Simulation modeling is the process of creating and analyzing a digital prototype of a physical model to predict its performance in the real world. Simulation modeling is used to help designers and engineers understand whether, under what conditions, and in which ways a part could fail and what loads it can withstand.
Modeling and simulation (M&S) is the use of models (e.g., physical, mathematical, behavioral, or logical representation of a system, entity, phenomenon, or process) as a basis for simulations to develop data utilized for managerial or technical decision making.
Live, Virtual, & Constructive (LVC) Simulation is a broadly used taxonomy for classifying Modeling and Simulation (M&S). However, categorizing a simulation as a live, virtual, or constructive environment is problematic since there is no clear division among these categories.
For example, LoFi data can be produced by models of a physical system that use approximations to simulate the system, rather than modeling the system in an exhaustive manner. [5] Moreover, in human-in-the-loop (HITL) situations the goal may be to predict the impact of technology on expert behavior within the real-world operational context.
A simulation is a way to implement the model, often employed when the model is too complex for the analytical solution. A steady-state simulation provides information about the system at a specific instant in time (usually at equilibrium, if such a state exists). A dynamic simulation provides information over time.
SPs can also serve as a "confederate" in a simulation to perform the roles of other clinicians within the care team. [4] SPs used for in situ simulation activities may require special training. [5] For teaching future healthcare professionals how to perform intimate examinations, a specially trained simulated patient may be used.
Derivative-free optimization is a subject of mathematical optimization. This method is applied to a certain optimization problem when its derivatives are unavailable or unreliable. Derivative-free methods establish a model based on sample function values or directly draw a sample set of function values without exploiting a detailed model.