Join us for the CPS Division Meeting today at 2:00PM CT
Speaker: Nesar Ramachandra
Title: Scientific Machine Learning for Astrophysical Studies
Abstract: Significant amount of machine learning (ML) applications on scientific data have hinted at the remarkable versatility of artificial intelligence (AI) in the era of exa-scale computation and
data-driven modeling. However, unique challenges in Cosmology require a transition from heuristic applications of ML frameworks to domain-aware AI systems. A subset of these crucial issues will be discussed in this talk, with an emphasis on the adaptation
of numerical simulations, explainability of AI models, uncertainty quantification, and scalability with state-of-the-art computational architectures. Bayesian inference of cosmological parameters using surrogate modeling, redshift estimation of galaxies, strong
lensing studies and other key topics at the intersection of computation cosmology and scientific machine learning will be explored.
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