30 citations · 31 across the 5 of their papers we have counts for
5 papers
An adaptive discretization algorithm for locally optimal experimental design with constraints
Jochen Schmid, Philipp Seufert, Jan Schwientek +2
We develop a novel iterative algorithm for locally optimal experimental design under constraints, like budget or performance constraints. It is an adaptive discretization algorithm…
Adaptive discretization algorithms for locally optimal experimental design
Jochen Schmid, Philipp Seufert, Michael Bortz
We develop adaptive discretization algorithms for locally optimal experimental design of nonlinear prediction models. With these algorithms, we refine and improve a pertinent state…
Computing T-optimal designs via nested semi-infinite programming and twofold adaptive discretization
David Mogalle, Philipp Seufert, Jan Schwientek +2
Modeling real processes often results in several suitable models. In order to be able to distinguish, or discriminate, which model best represents a phenomenon, one is interested,…
Two-phase approaches to optimal model-based design of experiments: how many experiments and which ones?
Charlie Vanaret, Philipp Seufert, Jan Schwientek +5
Model-based experimental design is attracting increasing attention in chemical process engineering. Typically, an iterative procedure is pursued: an approximate model is devised, p…
An Adaptive Algorithm based on High-Dimensional Function Approximation to obtain Optimal Designs
Philipp Seufert, Jan Schwientek, Michael Bortz
Algorithms which compute locally optimal continuous designs often rely on a finite design space or on repeatedly solving a complex non-linear program. Both methods require extensiv…