This paper establishes a connection between non-convex optimization and nonlinear partial differential equations (PDEs). We interpret empirically successful relaxation techniques motivated from ...

Learning data-driven discretizations for partial differential equations Code associated with the paper: Learning data-driven discretizations for partial differential equations. Yohai Bar-Sinai, ...

For dissipative partial differential equations the Lumer-Phillips generation theorem characterizes solvability and also boundedness of the associated semigroup. An extension of the Lumer-Phillips ...

Differential privacy adds strategic noise to datasets while preserving statistical value and providing formal privacy protection.