1 citations · 3 across the 8 of their papers we have counts for
3 papers · 1 filter
Cubature-based uncertainty estimation for nonlinear regression models
Martin Bubel, Jochen Schmid, Maximilian Carmesin +3
Calibrating model parameters to measured data by minimizing loss functions is an important step in obtaining realistic predictions from model-based approaches, e.g., for process op…
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…
Sequential optimal experimental design for vapor-liquid equilibrium modeling
Martin Bubel, Jochen Schmid, Volodymyr Kozachynskyi +2
We propose a general methodology of sequential locally optimal design of experiments for explicit or implicit nonlinear models, as they abound in chemical engineering and, in parti…