6 papers
Statistical Parameter Calibration via the Generalized Fluctuation Dissipation Theorem and Generative Modeling
Ludovico T. Giorgini, Tobias Bischoff, Andre N. Souza
We introduce a response-theoretic framework that recasts parameter calibration of ergodic stochastic differential equations as a fluctuation-dissipation problem. Our central result…
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…
KGMM: A K-means Clustering Approach to Gaussian Mixture Modeling for Score Function Estimation
Ludovico T. Giorgini, Tobias Bischoff, Andre N. Souza
We propose a hybrid method for accurately estimating the score function, i.e., the gradient of the log steady-state density, using a Gaussian Mixture Model (GMM) in conjunction wit…
Surface to Seafloor: A Generative AI Framework for Decoding the Ocean Interior State
Andre N. Souza, Simone Silvestri, Katherine Deck +3
Understanding subsurface ocean dynamics is essential for quantifying oceanic heat and mass transport, but direct observations at depth remain sparse due to logistical and technolog…
Reduced-Order Modeling of Cyclo-Stationary Time Series Using Score-Based Generative Methods
Ludovico Theo Giorgini, Tobias Bischoff, Andre Noguiera Souza
Many natural systems exhibit cyclo-stationary behavior characterized by periodic forcing such as annual and diurnal cycles. We present a data-driven method leveraging recent advanc…
Response Theory via Generative Score Modeling
Ludovico Theo Giorgini, Katherine Deck, Tobias Bischoff +1
We introduce an approach for analyzing the responses of dynamical systems to external perturbations that combines score-based generative modeling with the Generalized Fluctuation-D…