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

该奖项全部或部分根据2021年美国救援计划法案（公法117-2）资助。本科生的研究经验（REU）网站在得克萨斯州格兰德河谷的大学，将在九个星期的过程中接待八名学生在应用数学，数学建模和计算和数据科学的小组研究项目合作，应用理论模型的物理和生物现象。学生将通过接受数值方法及其编码培训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，并更好地为STEM的研究生课程和职业生涯做好准备。其中一个目标是使STEM领域多样化，计划让50%以上的参与者来自STEM中代表性不足的少数民族和代表性不足的群体。该计划还将为早期职业教师提供指导，使他们能够专业成长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，在期刊上发表论文以及免费提供的计算机软件编码来传播研究成果。 该REU计划将为本科生提供充满活力，激励性，最重要的是在随机和确定性偏微分方程和数值分析领域的高质量合作研究经验，重点是计算方面。此外，学生将开发和/或应用深度空间学习算法从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和引人入胜的主题，如散射波的动力学和非线性波在非均匀介质中的传播。作为第二个目标，学生将致力于模拟空间过程，如疾病的传播或污染的排放，这是使用偏微分方程与时空白色或彩色噪声建模。学生还将产生预测模型，并将其结果与空间过程的实际模型进行比较。了解如何在计算上解决每个模型将对学生解决其他数学模型的能力产生广泛的影响，并在申请研究生课程以及行业中具有竞争力。此外，学生将通过科学写作，演讲技巧和研究生院准备的研讨会在学术上得到丰富。该奖项反映了NSF的法定使命，并被认为值得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来支持。

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.