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Found 4 papers, 1 papers with code
Physics-informed Bayesian inference of external potentials in classical density-functional theory
We combine a Bayesian inference approach with the classical DFT apparatus to reconstruct the external potential, yielding a probabilistic description of the external potential functional form with inherent uncertainty quantification.
Physics-constrained Bayesian inference of state functions in classical density-functional theory
In our case, the output of the learning algorithm is a probability distribution over a family of free energy functionals, consistent with the observed particle data.
Enhancement of damaged-image prediction through Cahn-Hilliard Image Inpainting
We then compare the prediction accuracy of the neural network with and without applying the Cahn-Hilliard filter to the damaged images test.
Well-balanced finite volume schemes for hydrodynamic equations with general free energy
Well balanced and free energy dissipative first- and second-order accurate finite volume schemes are proposed for a general class of hydrodynamic systems with linear and nonlinear damping.
Numerical Analysis Statistical Mechanics Applied Physics Computational Physics Fluid Dynamics 35Lxx, 65Yxx, 65Zxx, 82Bxx, 82Dxx
