1 citations · 3 across the 7 of their papers we have counts for
4 papers · 1 filter
A Renormalization-Group Hierarchy of Stochastic Effective Dynamics Learned through Path Integrals
Yuki Yasuda, Tobias Bischoff
This study combines a stochastic renormalization group (RG) in space with a path-integral description of the time evolution, giving a spatial hierarchy of coarse-grained dynamics t…
Quantifying internal variability in large-ensemble climate data with the Wasserstein distance
Yuki Yasuda, Shoichiro Kido
Quantifying forced and internal variability in climate data is fundamental to detecting the climate change signal and assessing uncertainty in climate projections. We propose a met…
Uncertainty-Aware Spatiotemporal Super-Resolution Data Assimilation with Diffusion Models
Aditya Sai Pranith Ayapilla, Kazuya Miyashita, Yuki Yasuda +1
Data assimilation (DA) improves prediction of chaotic systems by combining model forecasts with sparse, noisy observations. Many DA methods are inherently probabilistic, but accura…
Predictor-Driven Diffusion for Spatiotemporal Generation
Yuki Yasuda, Tobias Bischoff
Multiscale spatial structure complicates temporal prediction because small-scale spatial fluctuations influence large-scale evolution, yet resolving all scales is often intractable…