5 papers
MPINeuralODE: Multiple-Initial-Condition Physics-Informed Neural ODEs for Globally Consistent Dynamical System Learning
Lake Yang, Antonio Malpica-Morales, Frank Ioannis Papadakis Wood +1
Neural ordinary differential equations (Neural ODEs) often fit training trajectories while generalizing poorly to unseen initial conditions and long horizons. We propose MPINeuralO…
A Finite Element Method for Fluctuating Navier--Stokes Equations
Dimitrios Gourzoulidis, Mirko Gallo, Soumaya Elkantassi +2
We introduce a finite-element framework for simulating thermal fluctuations in compressible fluids governed by the fluctuating Navier-Stokes equations. The method is designed to pr…
A Physics-Informed Neural Network with a Modified Lorentzian Activation for Nonlocal Gradient-Flow Equations in Dynamic Density Functional Theory
Dimitrios Gourzoulidis, Soumaya Elkantassi, Serafim Kalliadasis
We develop a physics-informed neural network (PINN) framework for nonlocal partial differential equations arising in dynamic density functional theory (DDFT). Such equations are ch…
Orientable Surfactants on Thin Liquid Films: A Dynamic Density-Functional Theory Approach
Toby Kay, Serafim Kalliadasis
Thin liquid films are ubiquitous across many natural and engineering systems, including films which are laden with surface active molecules, i.e. surfactants. The presence of surfa…
Hydrodynamic density-functional theory for the moving contact-line problem reveals fluid structure and emergence of a spatially distinct pattern
Andreas Nold, Benjamin D. Goddard, David N. Sibley +1
Understanding the nanoscale effects controlling the dynamics of a contact line -- defined as the line formed at the junction of two fluid phases and a solid -- has been a longstand…