Scientific Machine Learning: Introduction to Spin Glass Models
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Explores decision-making under uncertainty, focusing on Kilian Schindler's posthumous PhD thesis on scalable stochastic optimization and scenario reduction.
Explores the application of statistical physics in computational problems, covering topics such as Bayesian inference, mean-field spin glass models, and compressed sensing.
Explores phase transitions in physics and computational problems, highlighting challenges faced by algorithms and the application of physics principles in understanding neural networks.
