An efficient approximate solution for stochastic Lanchester models
针对复杂兰彻斯特战斗模型难以解析求解的问题，提出了矩匹配和战斗结束近似两种方法，能在短时间内给出高精度近似解，适用于资源优化等计算密集型问题。
Combat modeling is one of the essential topics for military decision making. The Lanchester equation is a classic method for modeling warfare, and many variations have extended its limitations and relaxed its assumptions. As a model becomes more complex, solving it analytically becomes intractable or computationally expensive. Hence, we propose two approximation methods: moment-matching scheme and a supporting method called battle-end approximation. These methods give an approximate solution in a short amount of time, while maintaining a high level of accuracy in simulation results in terms of hypothesis testing and numerical verification. They can be applied to computationally intensive problems, such as optimal resource allocation and analysis with asymmetric power like snipers or stealth aircrafts.