Irregularly Sampled Time Series Interpolation for Binary Evolution Simulations Using Dynamic Time Warping

Wed, 15 Apr 2026 00:00:00 +0000

Ugur Demir, Philipp M Srivastava, Aggelos K. Katsaggelos, Vicky Kalogera, Santiago L Tapia, Manuel Ballester, Shamal Lalvani, Patrick Koller, Jeff J Andrews, Seth Gossage, Max M Briel, and Elizabeth Teng

Abstract

This research addresses the computational bottleneck of population synthesis by introducing a robust interpolation scheme for binary stars. Unlike single-star evolution, binary tracks are often irregularly sampled and plagued by misaligned evolutionary phases due to mutual interactions.

By employing Dynamic Time Warping (DTW), we generate a joint alignment that preserves the underlying microphysics across the entire track, enabling the rapid generation of high-fidelity binary models for large-scale simulations like POSYDON.

BibTeX Citation

@misc{demir2026irregularlysampledtimeseries,
 title={Irregularly Sampled Time Series Interpolation for Binary Evolution Simulations Using Dynamic Time Warping},
 author={Ugur Demir and Philipp M. Srivastava and Aggelos Katsaggelos and Vicky Kalogera and Santiago L. Tapia and Manuel Ballester and Shamal Lalvani and Patrick Koller and Jeff J. Andrews and Seth Gossage and Max M. Briel and Elizabeth Teng},
 year={2026},
 eprint={2604.13604},
 archivePrefix={arXiv},
 primaryClass={astro-ph.SR},
 url={https://arxiv.org/abs/2604.13604},
}

Learning the Stellar Structure Equations via Self-supervised Physics-Informed Neural Networks

Mon, 06 Apr 2026 00:00:00 +0000

Manuel Ballester, Santiago Lopez-Tapia, Seth Gossage, Patrick Koller, Philipp M Srivastava, Ugur Demir, Yongseok Jo, Almudena P Marquez, Christoph Würsch, Souvik Chakraborty, Vicky Kalogera, and Aggelos K. Katsaggelos

Abstract

This research introduces a mesh-free approach to solving the fundamental equations of stellar structure using Physics-Informed Neural Networks (PINNs). By enforcing hydrostatic and thermal equilibrium constraints directly within the loss function, we eliminate the need for traditional grid-based discretization used in solvers like MESA.

Our framework incorporates auxiliary neural surrogates for microphysics (opacities and equation of state), allowing the model to learn continuous, differentiable radial profiles across a wide range of stellar masses.

BibTeX Citation

@misc{ballester2026learningstellarstructure,
 title={Learning the Stellar Structure Equations via Self-supervised Physics-Informed Neural Networks},
 author={Manuel Ballester and Santiago Lopez-Tapia and Seth Gossage and Patrick Koller and Philipp M. Srivastava and Ugur Demir and Yongseok Jo and Almudena P. Marquez and Christoph Wuersch and Souvik Chakraborty and Vicky Kalogera and Aggelos Katsaggelos},
 year={2026},
 eprint={2604.06255},
 archivePrefix={arXiv},
 primaryClass={astro-ph.SR},
 url={https://arxiv.org/abs/2604.06255},
}

Article | Patrick Koller

Irregularly Sampled Time Series Interpolation for Binary Evolution Simulations Using Dynamic Time Warping

Abstract

BibTeX Citation

Learning the Stellar Structure Equations via Self-supervised Physics-Informed Neural Networks

Abstract

BibTeX Citation