Explores turbulence characteristics, simulation methods, and modeling challenges, providing guidelines for choosing and validating turbulence models.
Covers gradient line search methods and optimization techniques with an emphasis on Wolfe conditions and positive definiteness.
Covers advanced numerical analysis topics including deep neural networks and optimization methods.
Covers optimization methods without constraints, including gradient and line search in the quadratic case.
Explores total scattering and PDF analysis in materials science, covering in-situ synthesis, data analysis techniques, and applications in host-guest systems.
Covers the basics of image processing using Fiji software, focusing on managing files and applying filters.
Explores parametric spectrum estimation methods, including line and smooth spectra, and delves into heart rate variability analysis.
Explores water consumption data in Geneva, including charts on consumption and losses, available datasets, and data processing phases.
Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.
