Author Topic: Learning the Physics of Pattern Format ion from Images  (Read 224 times)

Pankaj Dey

  • Moderator
  • Team sea
  • *****
  • Karma: +113/-0
    • View Profile
  • Institute : Indian Institute of Science
  • Programming language : MATLAB, R
Learning the Physics of Pattern Format ion from Images
« on: February 15, 2020, 11:14:31 AM »
Using a framework of partial differential equation-constrained optimization, we demonstrate that multiple constitutive relations can be extracted simultaneously from a small set of images of pattern formation. Examples include state-dependent properties in phase-field models, such as the diffusivity, kinetic prefactor, free energy, and direct correlation function, given only the general form of the Cahn-Hilliard equation, Allen-Cahn equation, or dynamical density functional theory (phase-field crystal model). Constraints can be added based on physical arguments to accelerate convergence and avoid spurious results. Reconstruction of the free energy functional, which contains nonlinear dependence on the state variable and differential or convolutional operators, opens the possibility of learning nonequilibrium thermodynamics from only a few snapshots of the dynamics.
https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.124.060201
Pankaj