deterministic model

简明释义

确定性模型

英英释义

例句

1.The stock market is complex, but some analysts attempt to create a deterministic model (确定性模型) to forecast price movements.

股市是复杂的，但一些分析师试图创建一个 确定性模型 (deterministic model) 来预测价格波动。

2.In climate science, a deterministic model (确定性模型) can predict weather patterns based on initial conditions.

在气候科学中，确定性模型 (deterministic model) 可以根据初始条件预测天气模式。

3.Engineers often use a deterministic model (确定性模型) to simulate the performance of structures under various loads.

工程师通常使用 确定性模型 (deterministic model) 来模拟结构在不同载荷下的性能。

4.The traffic flow was analyzed using a deterministic model (确定性模型) to improve congestion management.

交通流量通过使用 确定性模型 (deterministic model) 进行分析，以改善拥堵管理。

5.A deterministic model (确定性模型) in operations research can optimize resource allocation in manufacturing processes.

运筹学中的 确定性模型 (deterministic model) 可以优化制造过程中的资源分配。

作文

In the realm of science and mathematics, the term deterministic model refers to a representation of a system where the outcomes are precisely determined by the initial conditions and the governing laws. Unlike stochastic models, which incorporate randomness and uncertainty, a deterministic model provides a clear-cut prediction of future states based on its inputs. This characteristic makes it particularly useful in various fields, including physics, engineering, and economics.One of the most prominent examples of a deterministic model can be found in classical mechanics. The equations of motion formulated by Sir Isaac Newton illustrate how the position and velocity of an object can be predicted with absolute certainty if its initial conditions are known. For instance, if a ball is thrown into the air with a specific angle and speed, a deterministic model can accurately calculate its trajectory, maximum height, and time of flight. This predictability allows engineers to design structures and vehicles that rely on precise calculations, ensuring safety and efficiency.In contrast, when dealing with complex systems such as weather forecasting, a deterministic model may not always provide accurate predictions. Weather systems are influenced by numerous variables and exhibit chaotic behavior, making them inherently unpredictable over long periods. However, short-term forecasts can still benefit from deterministic models based on current atmospheric conditions. By analyzing these conditions through mathematical equations, meteorologists can offer reliable forecasts for a few days ahead.Another application of deterministic models is in the field of economics. Economists often use these models to analyze market behaviors and predict the effects of policy changes. For example, a simple supply and demand model can determine the equilibrium price of a product based on fixed variables like consumer preferences and production costs. While these models can provide valuable insights, they also have limitations, as real-world scenarios often involve unforeseen factors that can alter outcomes.Despite their advantages, deterministic models can sometimes lead to oversimplifications. Critics argue that relying solely on these models may ignore the complexities and uncertainties present in real-life situations. Therefore, many researchers advocate for a hybrid approach that combines both deterministic and stochastic elements to create more robust models. This approach allows for a better understanding of systems that exhibit both predictable and random behaviors, leading to more accurate predictions and analyses.In conclusion, the concept of a deterministic model plays a crucial role in our understanding of various scientific and mathematical phenomena. Its ability to provide precise predictions based on initial conditions makes it a powerful tool in fields such as physics, engineering, and economics. However, it is essential to recognize the limitations of these models and consider incorporating elements of uncertainty to capture the complexities of real-world systems. As we continue to advance in our understanding of these systems, the integration of deterministic models with stochastic approaches will likely yield more comprehensive insights and improve our predictive capabilities.

在科学和数学领域，术语确定性模型指的是一种系统的表示，其中结果由初始条件和控制法则精确决定。与包含随机性和不确定性的随机模型不同，确定性模型基于其输入提供对未来状态的明确预测。这一特征使其在物理学、工程学和经济学等多个领域特别有用。一个最显著的确定性模型的例子可以在经典力学中找到。艾萨克·牛顿制定的运动方程说明了如果已知物体的初始条件，就可以绝对确定地预测其位置和速度。例如，如果一个球以特定角度和速度被扔向空中，确定性模型可以准确计算其轨迹、最大高度和飞行时间。这种可预测性使工程师能够设计依赖于精确计算的结构和车辆，从而确保安全和效率。相反，在处理天气预报等复杂系统时，确定性模型可能并不总是提供准确的预测。天气系统受众多变量的影响，并且表现出混沌行为，使其在较长时间内固有不可预测。然而，短期预测仍然可以从基于当前大气条件的确定性模型中受益。通过分析这些条件，气象学家可以提供几天内可靠的天气预报。确定性模型的另一个应用是在经济学领域。经济学家通常使用这些模型来分析市场行为并预测政策变化的影响。例如，一个简单的供需模型可以根据消费者偏好和生产成本等固定变量确定产品的均衡价格。尽管这些模型可以提供有价值的见解，但它们也有局限性，因为现实世界的场景往往涉及不可预见的因素，这些因素可能会改变结果。尽管有其优势，确定性模型有时可能导致过度简化。批评者认为，仅仅依赖这些模型可能忽视现实生活中存在的复杂性和不确定性。因此，许多研究人员提倡采用混合方法，将确定性元素与随机元素结合，以创建更强大的模型。这种方法可以更好地理解表现出可预测性和随机行为的系统，从而导致更准确的预测和分析。总之，确定性模型的概念在我们理解各种科学和数学现象中起着至关重要的作用。它基于初始条件提供精确预测的能力使其成为物理学、工程学和经济学等领域的强大工具。然而，必须认识到这些模型的局限性，并考虑结合不确定性元素，以捕捉现实世界系统的复杂性。随着我们对这些系统理解的不断深入，将确定性模型与随机方法相结合，可能会产生更全面的见解，并提高我们的预测能力。