2026-09-20T04:28:12Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/124391 2024-09-16 col_2142_5131 col_2142_14807 com_2142_5130 com_2142_14806 com_2142_8903

This thesis explores the application of deep learning techniques to two astrophysical problems: simplifying chemical kinetics calculations and reconstructing galaxy merger histories. A framework called Dengo is presented that can automatically generate chemical kinetics solvers from user-specified networks, enabling simplified integration of customized chemistry in simulations. The combination of neural ordinary differential equations and autoencoders is shown to be a promising approach for reducing the complexity of chemical kinetics simulations. Specifically, autoencoders identify the reduced reaction subspace while the neural ODE learn the latent space dynamics. This demonstrates the potential of using deep learning for reduced order modeling of complex chemical networks in astrophysics. For studying galaxy evolution, a conditional graph generative model is developed that can reconstruct the merger histories of observed galaxies from cosmological simulations. This allows identifying progenitors and formation pathways of galaxies across cosmic time. The model captures statistical properties of high-redshift progenitors and enables outlier detection and correlation identification. An overview of deep learning methods with a focus on techniques used in this thesis is also provided. The works demonstrate the potential of using deep learning for the selected problems in computational astrophysics and cosmic structure formation. Text eng https://hdl.handle.net/2142/124391 © 2024 Kwok Sun Tang Turk, Matthew Ricker, Paul Fields, Brian Narayan, Gautham 2024-05 application/pdf en text Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms The student, Kwok Sun Tang, accepted the attached license on 2024-04-24 at 11:35. The student, Kwok Sun Tang, submitted this Dissertation for approval on 2024-04-24 at 11:42. This Dissertation was approved for publication on 2024-04-26 at 12:54. DSpace SAF Submission Ingestion Package generated from Vireo submission #20608 on 2024-09-16 at 00:36:06 Machine learning applications in astrophysics: Reduced-order modelling for chemical kinetics and galaxy merger reconstruction with graph neural network Tang, Kwok Sun 2024-04-26 Astronomy Machine Learning Astrophysics Deep Learning Ph.D. Dissertation Astronomy University of Illinois at Urbana-Champaign Astronomy