RUI: Uncertainty reduction through better nonlinear particle filters

这个项目的目标是创建和实现新的方法来提高滤波的性能，或数据同化技术，应用于非线性，非高斯系统。计算统计学、人工智能和地球物理学中的许多问题都涉及到这样的系统，并利用专门的蒙特卡罗采样方法（称为粒子滤波器）进行数据分析和预测，以及更好地理解潜在现象。首席研究员研究了变分数据同化方法，以证明使用这些方法生成目标系综可以提供更有效的滤波性能，并且动态适应粒子系综的大小可以提高滤波器的计算效率。统计计算是解决人工智能、工业和消费电子、机器人、天气系统、气候变化、海洋生态系统和陆地表面过程等不同领域问题的重要工具。拟议的研究改进了分析、估计和预测随时间推移而来的信息所需的统计计算，并有助于更好地将理论模型与观测数据结合起来。此外，本项目还涉及本科生应用科学研究能力的培养。

The objective of this project is to create and implement new methods for improving the performance of filtering, or data assimilation techniques, applied to non-linear, non-Gaussian systems. Many problems in computational statistics, artificial intelligence, and geophysics involve such systems, and utilize specialized Monte Carlo sampling methods, called particle filters, for data analysis and forecasting, and for better understanding of the underlying phenomena. The principal investigator studies variational data assimilation methods to demonstrate that targeted ensemble generation using those methods delivers more effective filter performance, and that dynamic adaptation of the size of the particle ensemble improves the computational efficiency of the filter.Statistical computations are an essential tool for the solution of problems in such diverse areas as artificial intelligence, industrial and consumer electronics, robotics, weather systems, climate change, ocean ecosystems, and land surface processes. The proposed research improves statistical computations required for the analysis, estimation, and forecasting of information that arrives over time, and contributes to a better combination of theoretical models with observational data. In addition, the project involves the training of undergraduate students in applied scientific research.