了解数值解析器对微分方程模型推理的影响
Richard Creswell1, Katherine M Shepherd1, Ben Lambert2
1Department of Computer Science, University of Oxford, Oxford, Oxfordshire, UK.
普通微分方程 (ODEs) 的数值解析器可以在参数推理中引起扭曲的概率表面,从而导致不准确的结果. 精心调整解决器公差对于可靠的ODE模型参数估计至关重要.
科学领域:
- 计算数学 计算数学 计算数学
- 数学建模的数学建模
- 科学计算科学计算
背景情况:
- 普通微分方程 (ODE) 模型在生物和物理科学中广泛使用.
- 数字方法对于解决ODEs至关重要,但近似可以引入错误.
- 对于前向模拟的准确性并不能保证对逆问题 (参数推理) 的准确性.
研究的目的:
- 调查数值解决器的不准确性如何影响ODE模型中的参数推理.
- 为了确定这些不准确性最严重的条件.
- 为可靠的推断提供关于设置解决器公差的指导.
主要方法:
- 固定和自适应阶段ODE解决器的分析.
- 由于数值近似而导致的可能表面扭曲的证明.
- 重新分析COVID-19 ODE变化点模型.
- 将水文降雨流失模型与数据相匹配.
主要成果:
- 解决器准确度不足可以创建的概率表面,将推理算法困在局部最佳状态中.
- 推断偏差在低噪音,快速的非线性动态系统中最为明显.
- 步骤大小显著影响ODE模型中的模拟和推理结果.
- 必须仔细调整解决器的公差,以避免扭曲的可能性.
结论:
- 在ODE解答器中,数值近似对参数推理构成重大挑战.
- 适应式步骤大小解决器的公差要求在推断过程中进行谨慎的设置.
- 建议检查概率表面是否存在数值工件.
- 确保解决器准确性对于可靠的ODE模型参数估计至关重要.
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