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Welcome to the Papers wiki!

This is a place to record papers, notes, documentation, etc. that aren't transient in issues.

• A recent review of all DA schemes and advances, Nice figures to get a sense of many techniques and issues!
• A more advanced paper from Alex that is cited above, as an approach to deal with high dimensions.
• Maeda/tdac: Successive estimation of a tsunami wavefield without earthquake source data: A data assimilation approach toward real‐time tsunami forecasting
• Maeda/tdac: Tsunami data assimilation of Cascadia seafloor pressure gauge records from the 2012 Haida Gwaii earthquake
• Gordon 1993 First bootstrap filter paper
• Local Ensemble Transform Kalman Filter: An Efficient Scheme for Assimilating Atmospheric Data
• Fearnhead and Künsch, 2018. Particle Filters and Data Assimilation. Annual Review of Statistics and Its Application
• Malleson et al, 2019. Simulating crowds in real time with agent-based modelling and a particle filter

Implementations

• GPU implementation of a particle filter, 2010, uses OpenGL
  • Main conclusion: Main bottlenecks are random number generation and cumulative sum of the CDF. These can be done by libraries modern GPUs.
  • Possibly an allgather of particles is also required?
  • They don't say anything about data movement which is a bit shady (pun intended).
• Fast, parallel implementation of particle filtering on the GPU architecture, 2013
• Cumulative sums in Cuda, Nvidia GPU Gems, Chapter 39