Introduces Scientific Machine Learning, emphasizing its application in various scientific fields and the connection between machine learning and physics.
Explores integrated risk management in civil engineering, including government policies, decision-making processes, and a controversial case study on risk analysis.
Covers theory and practical applications of protein folding simulations using molecular dynamics, focusing on solvent effects and analysis of folding dynamics.
Explores site choice, stakeholder involvement, and environmental impact assessments, emphasizing the challenges and complexities in decision-making processes.
Covers algorithmic paradigms for dynamic graph problems, including dynamic connectivity, expander decomposition, and local clustering, breaking barriers in k-vertex connectivity problems.
