3 papers
astro-ph.SR2026
Irregularly Sampled Time Series Interpolation for Binary Evolution Simulations Using Dynamic Time Warping
Ugur Demir, Philipp M. Srivastava, Aggelos Katsaggelos +9
Binary stellar evolution simulations are computationally expensive. Stellar population synthesis relies on these detailed evolution models at a fundamental level. Producing thousan…
astro-ph.SR2026
Learning the Stellar Structure Equations via Self-supervised Physics-Informed Neural Networks
Manuel Ballester, Santiago Lopez-Tapia, Seth Gossage +9
Stellar astrophysics relies critically on accurate descriptions of the physical conditions inside stars. Traditional solvers such as \texttt{MESA} (Modules for Experiments in Stell…
cs.CV2025
Caption-Driven Explainability: Probing CNNs for Bias via CLIP
Patrick Koller, Amil V. Dravid, Guido M. Schuster +1
Robustness has become one of the most critical problems in machine learning (ML). The science of interpreting ML models to understand their behavior and improve their robustness is…