Delves into the intersection of physics and data in machine learning models, covering topics like atomic cluster expansion force fields and unsupervised learning.
Covers the essentials of data science, including data handling, visualization, and analysis, emphasizing practical skills and active engagement.
Explores finite element methodology, covering geometric modeling, technical data, physical behavior assumptions, and mesh convergence studies.
Covers simulation, modeling, acceleration profiles, natural frequencies, rigidity calculations, and anti-resonance solutions for multi-axes robots.
