Hydrodynamic Models: Machine Learning for Kinetic Equations

Related lectures (30)

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Explores numerical methods for unsteady flow simulation, emphasizing time integration techniques and boundedness criteria.

Explores the dynamics of a pendulum, emphasizing equilibrium conditions and kinetic energy calculations for different points.

Covers numerical differentiation, forward differences, Taylor's expansion, Big O notation, and error minimization.

Explores turbulence characteristics, simulation methods, and modeling challenges, providing guidelines for choosing and validating turbulence models.

Explores numerical methods for solving the time-dependent Schrödinger equation using grid representation and split-operator algorithms.

Covers the numerical approach to derivative calculation, focusing on formulas and methods such as fine differences.

Explores unconfined flow in computational geomechanics, emphasizing weak form derivation and relative permeability.

Explores numerical methods for fluid dynamics and techniques for handling fluid interfaces.

Focuses on numerical modelling of atmospheric processes to predict weather and climate phenomena, covering key concepts and methods.

Covers the differential approach to fluid dynamics, focusing on conservation laws and the Cauchy stress tensor.