Nathan Waniorek
@nathanwaniorek.bsky.social
PhD student in applied math at UChicago. Working on inverse problems, data assimilation, and scientific machine learning.
https://sites.google.com/uchicago.edu/nathan-waniorek
https://sites.google.com/uchicago.edu/nathan-waniorek
Reposted by Nathan Waniorek
Struggling with your daily Letter Boxed?
Blame it on its hardness!
Blame it on its hardness!
Alessandro Giovanni Alberti, Flavio Chierichetti, Mirko Giacchini, Daniele Muscillo, Alessandro Panconesi, Erasmo Tani
Man, these New York Times games are hard! A computational perspective
https://arxiv.org/abs/2509.10846
Man, these New York Times games are hard! A computational perspective
https://arxiv.org/abs/2509.10846
September 16, 2025 at 11:38 AM
Struggling with your daily Letter Boxed?
Blame it on its hardness!
Blame it on its hardness!
Reposted by Nathan Waniorek
Finally compiled and refined this a bit; do take a look if you're curious! A more TeX'd version is attached in the screenshots.
hackmd.io/@sp-monte-ca...
'Some Intuition Boosters'
hackmd.io/@sp-monte-ca...
'Some Intuition Boosters'
December 30, 2024 at 3:42 PM
Finally compiled and refined this a bit; do take a look if you're curious! A more TeX'd version is attached in the screenshots.
hackmd.io/@sp-monte-ca...
'Some Intuition Boosters'
hackmd.io/@sp-monte-ca...
'Some Intuition Boosters'
New preprint! We prove long-time accuracy of ensemble Kalman filters for partially observed dynamical systems. We give conditions on the dynamics and observations that guarantee the estimation error remains small over the long-time asymptotic.
Daniel Sanz-Alonso, Nathan Waniorek
Long-time accuracy of ensemble Kalman filters for chaotic and machine-learned dynamical systems
https://arxiv.org/abs/2412.14318
Long-time accuracy of ensemble Kalman filters for chaotic and machine-learned dynamical systems
https://arxiv.org/abs/2412.14318
December 20, 2024 at 6:48 PM
New preprint! We prove long-time accuracy of ensemble Kalman filters for partially observed dynamical systems. We give conditions on the dynamics and observations that guarantee the estimation error remains small over the long-time asymptotic.