Simulation Studies of Tornadic Vortices

龙卷风涡的模拟研究

• 批准号: 8713846
• 负责人: John Case
• 金额: $ 11.02万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1987
• 资助国家: 美国
• 起止时间: 1987-09-01 至 1991-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8713846&HistoricalAwards=false
• 关键词: Simulation; Studies; Tornadic; Vortices

Insightful characterizations of the power of various machine self- reference principles, both in the context of universal programming systems and clocked systems, are sought with an eye to determining what use is self-knowledge (for programs). It is expected that machine self-knowledge is useful for machine self-modification (learning). Characterizations or strong sufficient conditions are sought especially for those self-reference principles which are algebraically well- behaved. Conditions are sought for validity of the principle that, if there is no self-referential counterexample to a proposition, the proposition is true. A rigorous mathematical solution is sought to the problem of why machine self-reference proofs lay bare an underlying, simplest reason for the theorems they prove. In the context of machine learning theory the P.I. is studying the effects of imposing complexity and succinctness constraints on final programs and grammars learned, the power of (formal) language learning machines if they converge on a small, uniformly, bounded, finite number of grammars, and the relationship of these and other matters to separability by sets enumerable by effective procedures which, in effect, change their "minds" finitely often. The tradeoffs between complexity of subrecursive text and speed of the inference progress are being investigated. In relatively small community applying recursion theory in computer science the P.I. is regarded as a leading figure.

对各种机器自身的力量进行了深刻的描述， 参考原则，无论是在普遍方案编制方面， 系统和时钟系统，寻求着眼于确定什么 使用是自我认知（对于程序）。 预计这台机器 自知识对于机器自修改（学习）是有用的。 特别是寻求了一些特征或强充分条件 对于那些在代数上很好的自指原理 表现良好 为原则的有效性寻求条件，如果 一个命题没有自指反例， 命题是真的。 一个严格的数学解决方案是寻求 为什么机器自引用证明暴露了一个潜在的问题， 他们证明的定理的最简单的理由。 在机器学习理论的背景下，P.I.正在研究 复杂性和简洁性约束对最终结果的影响 程序和语法学习，（正式）语言学习的力量 机器，如果他们收敛于一个小的，一致的，有界的，有限的数字， 的语法，以及这些和其他事项的关系， 可分离的集合可分离的有效程序，在 影响，经常改变他们的“想法”。 之间的权衡 子递归文本的复杂性和推理过程的速度是 正在调查 在相对较小的社区中， 科学的P.I.被视为领军人物。