Related papers: Dartmouth Stellar Evolution Emulator (DSEE) 1: Gen…
We build a semi-empirical framework of galaxy evolution (dubbed StAGE) firmly grounded on stellar archaeology. The latter provides data-driven prescriptions that, on a population statistical ground, allow to define the age and the star…
Diffusion models, which convert noise into new data instances by learning to reverse a Markov diffusion process, have become a cornerstone in contemporary generative modeling. While their practical power has now been widely recognized, the…
Creating noise from data is easy; creating data from noise is generative modeling. We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting…
Diffusion Probabilistic Models (DPMs) are a well-established class of diffusion models for unconditional image generation, while SGMSE+ is a well-established conditional diffusion model for speech enhancement. One of the downsides of…
Space-based photometry reveals regular high-frequency patterns in many young $\delta$ Scuti stars. These pulsations provide a powerful means of inferring stellar properties, particularly ages, for young $\delta$ Scuti stars for which…
Stochastic differential equations (SDEs) provide a flexible framework for modeling temporal dynamics in partially observed systems. A central task is to calibrate such models from data, which requires inferring latent trajectories and…
Understanding the ages of stars is crucial for unraveling the formation history and evolution of our Galaxy. Traditional methods for estimating stellar ages from spectroscopic data often struggle with providing appropriate uncertainty…
Policy targets evolve faster than the Coupled Model Intercomparison Project cycles, complicating adaptation and mitigation planning that must often contend with outdated projections. Climate model output emulators address this gap by…
We develop a class of non-Gaussian translation processes that extend classical stochastic differential equations (SDEs) by prescribing arbitrary absolutely continuous marginal distributions. Our approach uses a copula-based transformation…
Irregular sampling intervals and missing values in real-world time series data present challenges for conventional methods that assume consistent intervals and complete data. Neural Ordinary Differential Equations (Neural ODEs) offer an…
This work introduces InJecteD, a framework for interpreting Denoising Diffusion Probabilistic Models (DDPMs) by analyzing sample trajectories during the denoising process of 2D point cloud generation. We apply this framework to three…
In this paper, we address the issue of modeling and estimating changes in the state of the spatio-temporal dynamical systems based on a sequence of observations like video frames. Traditional numerical simulation systems depend largely on…
We introduce the Dynamical Cluster Assembly Framework (D-CAF), an AMUSE-based framework designed to connect embedded star formation histories to the dynamical evolution of young stellar systems. We model star formation through the gradual…
We present a novel generative modeling method called diffusion normalizing flow based on stochastic differential equations (SDEs). The algorithm consists of two neural SDEs: a forward SDE that gradually adds noise to the data to transform…
Numerous models have been developed for scanpath and saliency prediction, which are typically trained on scanpaths, which model eye movement as a sequence of discrete fixation points connected by saccades, while the rich information…
We focus on the problem of estimating the change in the dependency structures of two $p$-dimensional Gaussian Graphical models (GGMs). Previous studies for sparse change estimation in GGMs involve expensive and difficult non-smooth…
Recent advancements in stellar evolution modeling offer unprecedented accuracy in predicting the evolution and deaths of stars. We present new stellar evolutionary models computed with the updated PARSEC V2.0 code for a comprehensive and…
For many decades, dust has been recognised as an important ingredient in galaxy formation and evolution. This paper presents a novel self-consistent implementation of dust formation by stars, destruction by supernova shocks and hot gas, and…
Context: The pre-main sequence evolution is often simplified by choosing classical initial models. These have large initial radii and sufficient uniform contraction to make them fully convective. Contrary to that, real stars are born as…
This work proposes stochastic partial differential equations (SPDEs) as a practical tool to replicate clustering effects of more detailed particle-based dynamics. Inspired by membrane-mediated receptor dynamics on cell surfaces, we…