存储的随机动态系统中的密度演变:一种通用算法
1School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China.
Chaos (Woodbury, N.Y.)
|May 2, 2025
概括
本研究介绍了一种通用方法,用于计算存储的随机动态系统中的概率密度演变. 计算效率高的方法使用来自欧勒方案的离散模型,使得更广泛的应用.
科学领域:
- 动态系统和混沌理论
- 计算数学 计算数学 计算数学
- 气候科学 气候科学
背景情况:
- 具有内存的随机动态系统通常使用随机函数微分方程 (SFDE) 建模.
- 在SFDE中量化概率密度演变对于实际应用至关重要,但由于缺乏高效的计算方法,仍然具有挑战性.
- SFDE的一般形式限制了它们的广泛应用.
研究的目的:
- 提出一种通用且计算效率高的方法,用于计算具有内存的随机动态系统的广泛类别中的概率密度演变.
- 克服 SFDE 缺乏有效方法所造成的限制.
- 为了使这些复杂的系统能够得到更广泛的实际应用.
主要方法:
- 随机函数方程的近似,使用从欧勒方案衍生的离散模型.
- 通过计算离散对应的密度来递归估计概率密度.
- 拟议的方法是决定性的,并且在计算上高效.
主要成果:
- 成功计算了具有内存的随机动态系统的短暂和长期概率密度演变.
- 证明了新方法的有效性和效率.
- 在典型气候模型上验证了该方法.
结论:
- 开发的通用方法提供了一种高效和确定性的方法,用于计算存储的随机动态系统中的概率密度演变.
- 这种方法显著扩大了SFDE在各种科学领域的适用性,包括气候建模.
- 这种方法提供了一个强大的工具来分析复杂的系统,其中记忆效应是显著的.
相关概念视频
Genetic Drift
38.9K
Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
38.9K
Entropy Change in Reversible Processes
2.4K
In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
2.4K
Propagation of Uncertainty from Random Error
500
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
500
Basic Discrete Time Signals
179
The unit step sequence is defined as 1 for zero and positive values of the integer n. This sequence can be graphically displayed using a set of eight sample points, showing a step function starting from n=0 and remaining constant thereafter.
The unit impulse or sample sequence is mathematically expressed as zero for all n values except at n=0, where it is one. The unit impulse sequence, denoted by δ(n), is the first difference of the unit step sequence, while the unit step sequence u(n) is...
The unit impulse or sample sequence is mathematically expressed as zero for all n values except at n=0, where it is one. The unit impulse sequence, denoted by δ(n), is the first difference of the unit step sequence, while the unit step sequence u(n) is...
179
Second Order systems II
62
In an underdamped second-order system, where the damping ratio ζ is between 0 and 1, a unit-step input results in a transfer function that, when transformed using the inverse Laplace method, reveals the output response. The output exhibits a damped sinusoidal oscillation, and the difference between the input and output is termed the error signal. This error signal also demonstrates damped oscillatory behavior. Eventually, as the system reaches a steady state, the error diminishes to zero.
62
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
25
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
25


