REU Site: The University of Texas Rio Grande Valley REU Program on Applied Mathematics and Computational and Data Science

REU 网站：德克萨斯大学里奥格兰德河谷 REU 应用数学、计算和数据科学项目

• 批准号: 2150478
• 负责人: Erwin Suazo
• 金额: $ 32.41万
• 依托单位: The University of Texas Rio Grande Valley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2150478&HistoricalAwards=false
• 关键词: REU; Site; University; Texas; Rio

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). The Research Experiences for Undergraduates (REU) site at The University of Texas Rio Grande Valley, will host eight students over the course of nine weeks to work collaboratively on group research projects in applied mathematics, mathematical modeling and computational and data science, applying theoretical models to physical and biological phenomena. Students will acquire a wide range of computational skills by receiving training on numerical methods and their coding. The program will provide students with invaluable educational and research skills and experiences and better prepare them for graduate programs and careers in STEM. One of the goals is to diversify the STEM fields, with plans for more than 50% of participants coming from under-represented minorities and under-represented groups in STEM. The program will also provide mentoring to early-career faculty, allowing them to grow professionally as effective mentors for undergraduate research themselves. Students will disseminate research results by presenting at mathematics and interdisciplinary conferences, publishing papers in journals, and by coding computer software, which will be made freely available. This REU program will provide undergraduate students with vigorous, motivating, and above all quality collaborative research experience in the areas of stochastic and deterministic partial differential equations and numerical analysis with an emphasis on the computational aspects. Additionally, students will develop and/or apply deep spatial learning algorithms to spatial processes resulting from partial differential equations. As a first objective, students will study and analyze novel and engaging topics such as the dynamics of scattering waves and propagation of nonlinear waves in non-uniform media. As a second objective, students will work on simulating spatial processes such as the spread of diseases or emission of pollution, which are modeled using partial differential equations with space-time white or color noise. Students will also produce predictive models and compare their outcomes to actual models of spatial processes. Understanding how to address each model computationally will have a broad impact on the students’ ability to tackle other mathematical models and be competitive as applicants to graduate programs as well as in industry. Moreover, students will be enriched academically with workshops in scientific writing, presentation skills, and graduate school readiness.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.

该奖项全部或部分由《2021 年美国救援计划法案》（公法 117-2）资助，德克萨斯大学里奥格兰德谷分校的本科生研究体验 (REU) 网站将接待 8 名学生参加该课程。为期九周的合作研究项目涉及应用数学、数学建模以及计算和数据科学，将理论模型应用于物理和生物现象，学生将通过接受数值方法及其编码的培训来获得广泛的计算技能。该计划将为学生提供宝贵的教育和研究技能和经验，并为他们在 STEM 领域的研究生课程和职业做好更好的准备，目标之一是使 STEM 领域多样化，计划让超过 50% 的参与者来自代表性不足的少数族裔和群体。该计划还将为职业生涯早期的教师提供指导，使他们成为本科生研究的有效导师，学生将通过在数学和跨学科会议上发表论文来传播研究成果。期刊，并通过编码计算机软件，该项目将免费提供给本科生在随机和确定性偏微分方程和数值分析领域提供充满活力的、激励性的、最重要的高质量合作研究经验。在计算方面，学生将开发和/或将深度空间学习算法应用于偏微分方程产生的空间过程。作为第一个目标，学生将研究和分析新颖且引人入胜的主题，例如附加散射波和传播的动力学。的非线性波作为第二个目标，学生将致力于模拟疾病传播或污染排放等空间过程，这些过程使用时空白噪声或色噪声的偏微分方程进行建模。并将其结果与空间过程的实际模型进行比较。了解如何以计算方式解决每个模型将对学生处理其他数学模型的能力产生广泛的影响，并提高学生申请研究生课程和行业的竞争力。将通过科学写作研讨会来丰富学术，该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。