6 papers
Learning Aerosol Coagulation Dynamics in Latent Space with a Scale-Covariant Neural ODE
Wenhan Tang, Ruqi Yang, Jeffrey H. Curtis +5
Particle-resolved aerosol models preserve the joint size-composition structure that governs cloud activation, optical properties, and freezing, but their computational cost limits…
AeroMELD: A Linear Embedding of Aerosol Populations for Diagnostics and Latent Dynamics
Ehsan Saleh, Saba Ghaffari, Wenhan Tang +5
Accurately representing atmospheric aerosol populations is essential for simulating aerosol-cloud interactions, radiative forcing, and ice nucleation, yet existing reduced schemes…
Controller-Augmented Hidden Markov Models: A Computational Framework for Constrained Sequential Inference
Lekha Patel, Luis Damiano
Hidden Markov models are foundational for sequential inference, but their Markovian assumption fails under pathwise constraints such as precedence requirements, visitation cardinal…
Integrative Learning of Dynamically Evolving Multiplex Graphs and Nodal Attributes Using Neural Network Gaussian Processes with an Application to Dynamic Terrorism Graphs
Jose Rodriguez-Acosta, Sharmistha Guha, Lekha Patel +1
Exploring the dynamic co-evolution of multiplex graphs and nodal attributes is a compelling question in criminal and terrorism networks. This article is motivated by the study of d…
Reconstructing the Aerosol State from Partial Observations with Generative Modeling
E. Saleh, S. Ghaffari, J. H. Curtis +4
Key aerosol properties that shape climate -- such as CCN activity, scattering and absorption, and ice nucleation efficiency -- are difficult to infer from measurements that typical…
Generative Modeling of Aerosol State Representations
Ehsan Saleh, Saba Ghaffari, Jeffrey H. Curtis +4
Aerosol-cloud--radiation interactions remain among the most uncertain components of the Earth's climate system, in partdue to the high dimensionality of aerosol state representatio…