1 citations · 1 across the 4 of their papers we have counts for
14 papers
Conditional Score-Based Modeling of Effective Langevin Dynamics
Ludovico T. Giorgini
Stochastic reduced-order models are widely used to represent the effective dynamics of complex systems, but estimating their drift and diffusion coefficients from data remains chal…
Score-Based Modeling of Effective Langevin Dynamics
Ludovico Theo Giorgini
We introduce a constructive framework to learn effective Langevin equations from stationary time series. Unlike conventional approaches that require iterative calibration to match…
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…
Stochastic coupling of climate variables and ice volume over the Late Pleistocene glacial cycles
Pijush Patra, Ludovico T. Giorgini, J. S. Wettlaufer
Understanding the interactions between ice sheets and global climate forcings over geological timescales is essential for projecting their future. Previous studies have highlighted…
Integrating Score-Based Generative Modeling and Neural ODEs for Accurate Representation of Multiscale Chaotic Dynamics
Giulio Del Felice, Ludovico Theo Giorgini
Multiscale dynamical systems characterized by interacting fast and slow processes are ubiquitous across scientific domains, from climate dynamics to fluid mechanics. Accurate model…
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…