no code implementations • 2 Aug 2022 • Emilia Magnani, Nicholas Krämer, Runa Eschenhagen, Lorenzo Rosasco, Philipp Hennig
Neural operators are a type of deep architecture that learns to solve (i. e. learns the nonlinear solution operator of) partial differential equations (PDEs).
no code implementations • 25 Sep 2017 • Emilia Magnani, Hans Kersting, Michael Schober, Philipp Hennig
Recently there has been increasing interest in probabilistic solvers for ordinary differential equations (ODEs) that return full probability measures, instead of point estimates, over the solution and can incorporate uncertainty over the ODE at hand, e. g. if the vector field or the initial value is only approximately known or evaluable.