First Passage Percolation and Related Models
第一通道渗滤及相关模型
基本信息
- 批准号:2002388
- 负责人:
- 金额:$ 2万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2020
- 资助国家:美国
- 起止时间:2020-06-01 至 2023-05-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
The workshop First-Passage Percolation and Related Models will take place at the International Center for Theoretical Sciences in Bangalore, India, July 27-Aug 14, 2020. This workshop will be devoted to presenting the latest research progress on the first-passage percolation (FPP), a probabilistic model of random growth, and closely related models. This is currently an active research area in contemporary probability theory and has important connections to statistical physics. Part of the workshop will be devoted to training graduate students and early career researchers in those topics. This award will provide travel support for the US based participants in the workshop.In two dimensions, FPP is a model that is conjecturally in the KPZ universality class, which consists of several models describing surface growth processes. Models in the KPZ class are intimately related with random matrix theory, asymptotic representation theory and number theory. FPP and related models have also been studied by physicists as a model for polymers in a random medium or as model of magnetic interfaces, and by biologists as the Eden model of bacterial growth. Much progress has been made on a class of these relatives which are in some senses "exactly solvable" by the methods of integrable probability. However, even at the physics level of heuristic, these models are poorly understood outside of solvable special cases; for instance, there are hardly any results for dimensions larger than 2. Unlike other conferences on FPP-type models, the ICTS workshop will emphasize methods outside of the integrable or exactly solvable framework. By focusing on techniques beyond exact solvability, the workshop will unify participants around solving fundamental problems of interest to the broader scientific community. The workshop website is maintained at https://www.icts.res.in/program/FPP2020.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
研讨会第一通道渗滤和相关模型将于2020年7月27日至8月14日在印度班加罗尔的国际理论科学中心举行。本次研讨会将致力于介绍首次通过渗流(FPP),随机增长的概率模型,以及密切相关的模型的最新研究进展。 这是当代概率论中一个活跃的研究领域,与统计物理学有着重要的联系。讲习班的一部分将专门用于培训研究生和这些专题的早期职业研究人员。该奖项将为美国的研讨会参与者提供旅行支持。在两个维度上,FPP是KPZ通用类中的一个模型,它由几个描述表面生长过程的模型组成。KPZ类模型与随机矩阵理论、渐近表示理论和数论密切相关。FPP和相关模型也被物理学家作为随机介质中聚合物的模型或磁性界面的模型进行研究,并被生物学家作为细菌生长的伊甸园模型进行研究。对于一类在某种意义上可以用可积概率方法“精确可解”的相关问题,已经取得了很大的进展。然而,即使在物理学的启发式水平上,这些模型在可解的特殊情况之外也很难理解;例如,对于大于2的维度几乎没有任何结果。与其他关于FPP类型模型的会议不同,ICTS研讨会将强调可集成或可精确求解框架之外的方法。通过关注精确可解性之外的技术,研讨会将使与会者团结起来,解决更广泛的科学界感兴趣的基本问题。该研讨会网站维护在https://www.icts.res.in/program/FPP2020.This奖项反映了NSF的法定使命,并已被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估的支持。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Arjun Krishnan其他文献
Nanostructured Organogels via Molecular Self‐Assembly
通过分子自组装的纳米结构有机凝胶
- DOI:
- 发表时间:
2010 - 期刊:
- 影响因子:0
- 作者:
Arjun Krishnan;Kristen E. Roskov;R. Spontak - 通讯作者:
R. Spontak
Renewal-Reward Process Formulation of Motor Protein Dynamics
运动蛋白动力学的更新奖励过程公式
- DOI:
- 发表时间:
2011 - 期刊:
- 影响因子:3.5
- 作者:
Arjun Krishnan;B. Epureanu - 通讯作者:
B. Epureanu
Predicting High-Risk Plaques in Familial Hypercholesterolemia Using Clinical Variables and Coronary Artery Calcium
使用临床变量和冠状动脉钙预测家族性高胆固醇血症的高风险斑块
- DOI:
- 发表时间:
2022 - 期刊:
- 影响因子:0
- 作者:
B. Jaltotage;A. Abraham;J. Pang;Arjun Krishnan;B. Chow;A. Ihdayhid;Juan Lu;G. Watts;G. Dwivedi - 通讯作者:
G. Dwivedi
A network-based drug repurposing approach identifies new treatment opportunities for the systemic chronic inflammation underlying multiple complex diseases
基于网络的药物再利用方法为多种复杂疾病背后的全身慢性炎症确定了新的治疗机会
- DOI:
- 发表时间:
2022 - 期刊:
- 影响因子:0
- 作者:
Stephanie L. Hickey;Alexander McKim;Christopher A Mancuso;Arjun Krishnan - 通讯作者:
Arjun Krishnan
Stationary coalescing walks on the lattice
网格上的固定聚结行走
- DOI:
10.1007/s00440-018-0893-2 - 发表时间:
2018 - 期刊:
- 影响因子:2
- 作者:
J. Chaika;Arjun Krishnan - 通讯作者:
Arjun Krishnan
Arjun Krishnan的其他文献
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{{ truncateString('Arjun Krishnan', 18)}}的其他基金
Collaborative Research: RESEARCH-PGR: Predicting Phenotype from Molecular Profiles with Deep Learning: Topological Data Analysis to Address a Grand Challenge in the Plant Sciences
合作研究:RESEARCH-PGR:利用深度学习从分子概况预测表型:拓扑数据分析应对植物科学的重大挑战
- 批准号:
2310357 - 财政年份:2023
- 资助金额:
$ 2万 - 项目类别:
Standard Grant
CAREER: Assigning comprehensive, standardized sample annotations to enhance the ability to discover, use, and interpret millions of –omics profiles
职业:分配全面、标准化的样本注释,以增强发现、使用和解释数百万个组学概况的能力
- 批准号:
2328140 - 财政年份:2022
- 资助金额:
$ 2万 - 项目类别:
Continuing Grant
CAREER: Assigning comprehensive, standardized sample annotations to enhance the ability to discover, use, and interpret millions of –omics profiles
职业:分配全面、标准化的样本注释,以增强发现、使用和解释数百万个组学概况的能力
- 批准号:
2045651 - 财政年份:2021
- 资助金额:
$ 2万 - 项目类别:
Continuing Grant
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0203943 - 财政年份:2002
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第一通道渗透和风景随机行走研究
- 批准号:
9815226 - 财政年份:1998
- 资助金额:
$ 2万 - 项目类别:
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