ENBIS-26 Conference

Session

Uncertainty quantification and computer experiments

Sep 8, 2026, 10:55 AM

Conveners

Uncertainty quantification and computer experiments

• Julien Pelamatti (EDF R&D)

Uncertainty quantification and computer experiments

• Julien Pelamatti (EDF R&D)

33. Prediction of physical fields under linear constraints

Mahamat Hamdan Nassouradine (Université Paris-Saclay, CEA, Service de Génie Logiciel pour la Simulation, France)

9/8/26, 10:55 AM

Uncertainty quantification and computer experiments

Metamodeling is a fundamental approach for approximating computationally expensive numerical simulations in engineering applications, such as uncertainty propagation and sensitivity analysis. In this work, we address the simultaneous prediction of multiple high-dimensional physical fields governed by linear equality constraints, a setting that arises naturally in problems involving...

41. Multifidelity Gaussian process regression for solving nonlinear partial differential equations

Fatima-Zahrae EL-BOUKKOURI (INSA Toulouse / IMT)

9/8/26, 11:15 AM

Uncertainty quantification and computer experiments

Kernel-based methods provide a principled alternative to classical numerical solvers for nonlinear partial differential equations (PDEs), especially in mesh-free settings with built-in regularization and uncertainty quantification. Traditional discretization techniques such as finite differences or finite elements can become computationally demanding for nonlinear or multiscale problems. In...

100. Taxonomy of statistical models for investigating order picking systems

Larissa Sander (Fachhochschule Dortmund)

9/8/26, 11:35 AM

Uncertainty quantification and computer experiments

Order picking systems are known as complex logistics systems, where uncertainties, e.g., caused by randomness of incoming orders or delay of supplies, are present. Investigating the relationship between input variables and key performance indicators (KPIs) in common types of order picking systems, described by reference models, is aimed. Input variables are for example system load, batch size...

48. Spectral Clustering for Detecting Stationary Subregions in Gaussian Process Regression

Théo Sylvestre (CEA Paris Saclay)

9/8/26, 12:00 PM

Uncertainty quantification and computer experiments

Complex physical systems are often modeled using high-fidelity simulation codes. However, their inherent complexity makes each simulation computationally expensive, which severely limits their direct use in tasks such as uncertainty quantification. To overcome this, a widely adopted solution is to approximate the simulator using a Gaussian Process Regression (GPR) surrogate model.

However,...

50. Multi-fidelity Gaussian processes for noisy outputs and non-nested experiment designs: a comparison between the recursive and non-recursive formulations

Nils Baillie (Université Paris-Saclay, CEA, Service d’Etudes Mécaniques et Thermiques, 91191 Gif-sur-Yvette, France)

9/8/26, 12:20 PM

Uncertainty quantification and computer experiments

Surrogate models provide fast approximations of computationally expensive simulations (or experiments) and are trained using a limited set of observations generated by these codes. In the multi-fidelity framework, we assume the availability of two computer models with different levels of cost and accuracy. The high-fidelity model $z_H$ provides the most accurate predictions but is also the...

105. Integrating Global Sensitivity Analysis into Bayesian Optimization for Tactical Decision Support in Transport Logistics

Lara Kuhlmann de Canaviri (Fachhochschule Dortmund)

9/8/26, 12:40 PM

Uncertainty quantification and computer experiments

Transport logistics facilities such as less-than-truckload terminals require fast and robust tactical decisions under uncertainty, for example regarding task scheduling, resource allocation, and terminal configuration. Since detailed simulation experiments are computationally expensive, surrogate-assisted optimization methods provide an important basis for decision support.

This...