Direct data driven nonlinear control design

We study the Riemannian structure of L 2 -Wasserstein metric in the probability simplex supported on finite graphs [11], a topic initialized in [9, 20, 22], and further extend

E.g., the transport Hessian gradient flow, including Newton’s flow, formulates a mean-field kernel Stein variational gradient flow; The transport Hessian Hamiltonian flow of

With predictive capabilities becoming always faster and allowing always more complex models, the limitations associated with the uncertainty in the parameters of the

For example, the consideration of variations of geometry would require changing the computational model (i.e. the finite element mesh) for every realization of the geometry.

Taking into account uncertainties in nonlinear multiscale methods implies (a) constructing a probabilistic surrogate macromodel from microcalculations, allowing for

For exam- ple, we fully describe in Proposition 4.6 the universal (complete shifted) homotopy Lie algebra mc 1 , which represents gauges between Maurer–Cartan elements, and which

As a consequence of Theorem 1.15 below, we will give prove a slightly stronger version of the theorem just proved (see Remark 1.16) showing that, given a family of smooth

Donc les nouvelles formules de récurrence obtenues par des résultats de cet article, qui sont plus simples que celles de Donkin, seront utiles pour les autres chercheurs dans la