1 citations · 3 across the 7 of their papers we have counts for
9 papers
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…
Probabilistic Super-Resolution for Urban Micrometeorology via a Schrödinger Bridge
Yuki Yasuda, Ryo Onishi
This study employs a neural network that represents the solution to a Schrödinger bridge problem to perform super-resolution of 2-m temperature in an urban area. Schrödinger bridge…
Zero-Shot Super-Resolution from Unstructured Data Using a Transformer-Based Neural Operator for Urban Micrometeorology
Yuki Yasuda, Ryo Onishi
This study demonstrates that a transformer-based neural operator (TNO) can perform zero-shot super-resolution of two-dimensional temperature fields near the ground in urban areas.…
Two-Stage Super-Resolution Simulation Method of Three-Dimensional Street-Scale Atmospheric Flows for Real-Time Urban Micrometeorology Prediction
Yuki Yasuda, Ryo Onishi
A two-stage super-resolution simulation method is proposed for street-scale air temperature and wind velocity, which considerably reduces computation time while maintaining accurac…
Unsupervised Super-Resolution Data Assimilation Using Conditional Variational Autoencoders with Estimating Background Covariances via Super-Resolution
Yuki Yasuda, Ryo Onishi
This study proposes a theory of unsupervised super-resolution data assimilation (SRDA) using conditional variational autoencoders (CVAEs). We derive an evidence lower bound for uns…
Super-Resolution of Three-Dimensional Temperature and Velocity for Building-Resolving Urban Micrometeorology Using Physics-Guided Convolutional Neural Networks with Image Inpainting Techniques
Yuki Yasuda, Ryo Onishi, Keigo Matsuda
Atmospheric simulations for urban cities can be computationally intensive because of the need for high spatial resolution, such as a few meters, to accurately represent buildings a…