30 citations · 48 across the 11 of their papers we have counts for
13 papers
Scalable High-Dimensional Bayesian Field Reconstruction with Finite Elements: Application to 3D Porous Media Flow
Jonas Nitzler, Maximilian Bergbauer, Phaedon-Stelios Koutsourelakis +1
We present a unified, finite-element-native variational inference framework for very high-dimensional Bayesian spatial field reconstruction in physics-based problems governed by pa…
Multi-Agent Framework Leveraging Knowledge Graphs for Virtual Commissioning Models
Max Diekmann, Jonas Nitzler, Jan Fischer +2
Virtual commissioning models (VCMs) of discrete manufacturing systems are used to validate automation behavior before physical deployment, but creating and maintaining them remains…
A Black Box Variational Inference Scheme for Inverse Problems with Demanding Physics-Based Models
G. Robalo Rei, C. P. Schmidt, J. Nitzler +2
Bayesian methods are particularly effective for addressing inverse problems due to their ability to manage uncertainties inherent in the inference process. However, employing these…
Multi-Physics-Enhanced Bayesian Inverse Analysis: Information Gain from Additional Fields
Lea J. Haeusel, Jonas Nitzler, Lea J. Köglmeier +1
Inverse analysis, such as model calibration, often suffers from a lack of informative data in complex real-world scenarios. The standard remedy, designing new experimental setups,…
QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models
Jonas Biehler, Jonas Nitzler, Sebastian Brandstaeter +7
A growing challenge in research and industrial engineering applications is the need for repeated, systematic analysis of large-scale computational models, for example, patient-spec…
Efficient Bayesian multi-fidelity inverse analysis for expensive and non-differentiable physics-based simulations in high stochastic dimensions
Jonas Nitzler, Bugrahan Z. Temür, Phaedon-Stelios Koutsourelakis +1
High-dimensional Bayesian inverse analysis (dim >> 100) is mostly unfeasible for computationally demanding, nonlinear physics-based high-fidelity (HF) models. Usually, the use of m…
Bayesian Windkessel calibration using optimized 0D surrogate models
Jakob Richter, Jonas Nitzler, Luca Pegolotti +6
Boundary condition (BC) calibration to assimilate clinical measurements is an essential step in any subject-specific simulation of cardiovascular fluid dynamics. Bayesian calibrati…
Solving Bayesian Inverse Problems With Expensive Likelihoods Using Constrained Gaussian Processes and Active Learning
Maximilian Dinkel, Carolin M. Geitner, Gil Robalo Rei +2
Solving inverse problems using Bayesian methods can become prohibitively expensive when likelihood evaluations involve complex and large scale numerical models. A common approach t…
Bayesian calibration of coupled computational mechanics models under uncertainty based on interface deformation
Harald Willmann, Jonas Nitzler, Sebastian Brandstaeter +1
Calibration or parameter identification is used with computational mechanics models related to observed data of the modeled process to find model parameters such that good similari…
Global sensitivity analysis based on Gaussian-process metamodelling for complex biomechanical problems
Barbara Wirthl, Sebastian Brandstaeter, Jonas Nitzler +2
Biomechanical models often need to describe very complex systems, organs or diseases, and hence also include a large number of parameters. One of the attractive features of physics…
Physics-Based Modeling and Predictive Simulation of Powder Bed Fusion Additive Manufacturing Across Length Scales
Christoph Meier, Sebastian L. Fuchs, Nils Much +10
Powder bed fusion additive manufacturing (PBFAM) of metals has the potential to enable new paradigms of product design, manufacturing and supply chains while accelerating the reali…
A Novel Physics-Based and Data-Supported Microstructure Model for Part-Scale Simulation of Laser Powder Bed Fusion of Ti-6Al-4V
Jonas Nitzler, Christoph Meier, Kei W. Müller +2
The elasto-plastic material behavior, material strength and failure modes of metals fabricated by additive manufacturing technologies are significantly determined by the underlying…
A Generalized Probabilistic Learning Approach for Multi-Fidelity Uncertainty Propagation in Complex Physical Simulations
Jonas Nitzler, Jonas Biehler, Niklas Fehn +2
Two of the most significant challenges in uncertainty quantification pertain to the high computational cost for simulating complex physical models and the high dimension of the ran…