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相关概念视频

Theorems of Pappus and Guldinus: Problem Solving01:12

Theorems of Pappus and Guldinus: Problem Solving

744
Pappus and Guldinus's theorems are powerful mathematical principles that are used for finding the surface area and volume of composite shapes. For example, consider a cylindrical storage tank with a conical top. Finding the surface area or volume can be challenging for such complex shapes. These theorems are particularly useful in calculating the volume and surface area of such systems. Here, the cylindrical storage tank with a conical top can be broken down into two simple shapes: a...
744
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

56
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...
56
Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

381
Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
381
Language and Cognition01:27

Language and Cognition

349
Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
349
Castigliano's Theorem: Problem Solving01:14

Castigliano's Theorem: Problem Solving

658
The deflection of a simply supported beam that carries a central point load can be analyzed using structural mechanics principles, particularly by applying Castigliano's theorem. This theorem relates the displacement at the load application point to the partial derivatives of the strain energy in the structure. The simply supported beam with a point load at its center has symmetric reaction forces at the supports, each bearing half of the load. The bending moment at any point along the beam...
658
Machines: Problem Solving II01:30

Machines: Problem Solving II

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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相关实验视频

Updated: Jul 8, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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用大型语言模型进行程序搜索的数学发现

Bernardino Romera-Paredes1, Mohammadamin Barekatain2, Alexander Novikov2

  • 1Google DeepMind, London, UK. brp@google.com.

Nature
|December 14, 2023
PubMed
概括
此摘要是机器生成的。

大型语言模型 (LLM) 现在可以使用FunSearch进行科学发现,这是一种进化方法,它将LLM与评估器结合起来,以克服混和解决复杂的问题.

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The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition
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科学领域:

  • 人工智能
  • 组合学
  • 计算机科学

背景情况:

  • 大型语言模型 (LLM) 在复杂的任务中表现出色,但往往会混,限制它们的科学应用.
  • 现有的LLM方法由于不准确而难以进行科学发现.

研究的目的:

  • 介绍FunSearch,一个进化过程来增强科学发现的LLM能力.
  • 展示FunSearch在解决已确定的未解决问题的有效性.

主要方法:

  • 一个经过预先训练的LLM与一个系统的评估者进行进化搜索.
  • 将FunSearch应用到极端组合学 (上限设置问题) 和算法问题 (在线垃圾包装).

主要成果:

  • 在有限维度和异面性情况下发现了新的,改进的大型帽子集结构.
  • 确定了在线垃圾包装的新启发方法,其性能优于现有的基线.
  • 展示了FunSearch用于解决问题的可解释程序的能力.

结论:

  • 功能搜索使LLM能够在已知科学领域做出重大发现.
  • 这种方法克服了LLM的误解,提高了科学应用的可靠性.
  • FunSearch提供了一个可扩展和可解释的AI驱动科学发现方法.