7 papers
Domain-Decomposition Neural Surrogates for Scalable Decentralized Ensemble Kalman Filter Based Parameter Identification in High-Dimensional Stochastic PDEs
Timm Gödde, Bojana Rosić
Ensemble Kalman filters (EnKF) provide an efficient framework for parameter identification of physics based laws from spatially distributed measurements. Their forecast models requ…
Domain decomposition of large neural network surrogate models
Timm Gödde, Eisso H. Atzema, Bojana Rosić
Neural networks (NNs) have gained significant attention across various engineering disciplines, particularly in design optimization, where they are used to build surrogate models f…
Constitutive Manifold Neural Networks
Wouter J. Schuttert, Mohammed Iqbal Abdul Rasheed, Bojana Rosić
Anisotropic material properties, such as the thermal conductivities of engineering composites, exhibit variability due to inherent material heterogeneity and manufacturing-related…
Stochastic Modelling of Elasticity Tensor Fields
Sharana Kumar Shivanand, Bojana Rosić, Hermann G. Matthies
We present a novel framework for the probabilistic modelling of random fourth order material tensor fields, with a focus on tensors that are physically symmetric and positive defin…
Stochastic Modelling of Symmetric Positive Definite Material Tensors
Sharana Kumar Shivanand, Bojana Rosić, Hermann G. Matthies
Spatial symmetries and invariances play an important role in the behaviour of materials and should be respected in the description and modelling of material properties. The focus h…
Scale-invariant Monte Carlo and multilevel Monte Carlo estimation of mean and variance: An application to simulation of linear elastic bone tissue
Sharana Kumar Shivanand, Bojana Rosić
We propose novel scale-invariant error estimators for the Monte Carlo and multilevel Monte Carlo estimation of mean and variance. For any linear transformation of the distribution…
Inverse Problems in a Bayesian Setting
Hermann G. Matthies, Elmar Zander, Bojana V. Rosić +2
In a Bayesian setting, inverse problems and uncertainty quantification (UQ) --- the propagation of uncertainty through a computational (forward) model --- are strongly connected. I…