Related papers: Dartmouth Stellar Evolution Emulator (DSEE) 1: Gen…
One-dimensional (1D) methods for simulating the common-envelope (CE) phase offer advantages over three-dimensional (3D) simulations regarding their computational speed and feasibility. We present the 1D CE method from Bronner et al. (2024),…
Physical systems whose dynamics are governed by partial differential equations (PDEs) find applications in numerous fields, from engineering design to weather forecasting. The process of obtaining the solution from such PDEs may be…
The number density of UV luminous galaxies discovered by the James Webb Space Telescope at ultra high redshift ($z \gtrsim 10$) is higher, and declines much more slowly with increasing redshift, than expected from extrapolations of lower…
Systems governed by partial differential equations (PDEs) require computationally intensive numerical solvers to predict spatiotemporal field evolution. While machine learning (ML) surrogates offer faster solutions, autoregressive inference…
We present a database of the latest stellar models of the $Y^2$ (Yonsei-Yale) collaboration. This database contains the stellar evolutionary tracks from the pre-main-sequence birthline to the helium core flash that were used to construct…
Sparse-view computed tomography (CT) reconstruction is fundamentally challenging due to undersampling, leading to an ill-posed inverse problem. Traditional iterative methods incorporate handcrafted or learned priors to regularize the…
We propose Deterministic Sequencing of Exploration and Exploitation (DSEE) algorithm with interleaving exploration and exploitation epochs for model-based RL problems that aim to simultaneously learn the system model, i.e., a Markov…
Statistical systems are conceived from the standpoint of statistical mechanics, as made of a (generally large) number of identical units and exhibiting a (generally large) number of different configurations (microstates), among which only…
Simulations of dense stellar systems currently face two major hurdles, one astrophysical and one computational. The astrophysical problem lies in the fact that several major stages in binary evolution, such as common envelope evolution, are…
We introduce the star cluster evolution code Evolve Me A Cluster of StarS (EMACSS), a simple yet physically motivated computational model that describes the evolution of some fundamental properties of star clusters in static tidal fields.…
Diffusion-based generative models in SE(3)-invariant space have demonstrated promising performance in molecular conformation generation, but typically require solving stochastic differential equations (SDEs) with thousands of update steps.…
The last decade showed an impressive observational effort from the photometric and spectroscopic point of view for ancient stellar clusters in our Galaxy and beyond. The theoretical interpretation of these new observational results requires…
Clinical time series data from electronic health records and medical registries offer unprecedented opportunities to understand patient trajectories and inform medical decision-making. However, leveraging such data presents significant…
It takes years of effort employing the best telescopes and instruments to obtain high-quality stellar photometry, astrometry, and spectroscopy. Stellar evolution models contain the experience of lifetimes of theoretical calculations and…
This work introduces a novel probabilistic deep learning technique called deep Gaussian mixture ensembles (DGMEs), which enables accurate quantification of both epistemic and aleatoric uncertainty. By assuming the data generating process…
$\delta$ Scuti stars in binary or multiple systems serve as crucial probes for studying stellar pulsation and evolution. However, many such systems are not ideal for asteroseismology due to uncertainties in mass transfer with close…
Many real-world problems require reasoning across multiple scales, demanding models which operate not on single data points, but on entire distributions. We introduce generative distribution embeddings (GDE), a framework that lifts…
We present a framework and algorithms to learn controlled dynamics models using neural stochastic differential equations (SDEs) -- SDEs whose drift and diffusion terms are both parametrized by neural networks. We construct the drift term to…
An N-body code containing live stellar evolution through combination of the software packages NBODY6 and STARS is presented. Operational details of the two codes are outlined and the changes that have been made to combine them discussed. We…
We study the evolution of star clusters in the Galactic tidal field starting from their birth in molecular clumps. Our model clusters form according to the local-density-driven cluster formation model in which the stellar density profile is…