A5059975132- Probabilistic and Robust Engineering Design
- Aortic aneurysm repair treatments
- Model Reduction and Neural Networks
- Reservoir Engineering and Simulation Methods
- Elasticity and Material Modeling
- Structural Health Monitoring Techniques
- Cardiac, Anesthesia and Surgical Outcomes
- Aortic Disease and Treatment Approaches
- Inhalation and Respiratory Drug Delivery
- Advanced Multi-Objective Optimization Algorithms
- Statistical Methods and Inference
- Respiratory Support and Mechanisms
- Gaussian Processes and Bayesian Inference
- Fatigue and fracture mechanics
- Radiation Dose and Imaging
- Cardiovascular Function and Risk Factors
- demographic modeling and climate adaptation
- Systemic Sclerosis and Related Diseases
- Rheology and Fluid Dynamics Studies
- Cardiac Valve Diseases and Treatments
- Optimization and Mathematical Programming
- Capital Investment and Risk Analysis
- Tribology and Lubrication Engineering
- Fluid Dynamics and Vibration Analysis
- Connective tissue disorders research
Technical University of Munich
2014-2025
Bayesian boundary condition (BC) calibration approaches from clinical measurements have successfully quantified inherent uncertainties in cardiovascular fluid dynamics simulations. However, estimating the posterior distribution for all BC parameters three-dimensional (3D) simulations has been unattainable due to infeasible computational demand. We propose an efficient method identify Windkessel parameter posteriors: only evaluate 3D model once initial choice of BCs and use result create a...
Inhaled therapeutics have high potential for the treatment of chronic respiratory diseases unmet medical need, such as idiopathic pulmonary fibrosis (IPF). Preclinical and early clinical evidence show that cellular communication network factor 2 (CCN2), previously called connective tissue growth (CTGF), is a promising target IPF. In recent phase 3 trials, however, systemic CCN2 inhibition failed to demonstrate clinically meaningful benefit. Here, we present preclinical profile inhaled...
Little is known about the interactions between extracellular matrix (ECM) proteins and locally acting mechanical conditions material macroscopic properties in abdominal aortic aneurysm (AAA). In this study, ECM components were investigated with correlation to corresponding biomechanical loads aneurysmal arterial wall tissue.Fifty-four tissue samples from 31 AAA patients (30♂; max. diameter Dmax 5.98 ± 1.42 cm) excised sac. Samples divided for immunohistological analysis. Collagen I III,...
Abstract Growth and remodeling in arterial tissue have attracted considerable attention over the last decade. Mathematical models been proposed, computational studies with these helped to understand role of different model parameters. So far it remains, however, poorly understood how much output variability can be attributed individual input parameters their interactions. To clarify this, we propose herein a global sensitivity analysis, based on Sobol indices, for homogenized constrained...
Abstract Background Even an ultraprotective ventilation strategy in severe acute respiratory distress syndrome (ARDS) patients treated with extracorporeal membrane oxygenation (ECMO) might induce ventilator-induced lung injury and apneic the sole application of positive end-expiratory pressure may, therefore, be alternative strategy. We, compared effects on oxygenation, oxygen delivery, system mechanics, hemodynamics, strain, air distribution recruitment parenchyma ARDS ECMO. Methods In a...
If computational models are ever to be used in high-stakes decision making clinical practice, the use of personalized and predictive simulation techniques is a must. This entails rigorous quantification uncertainties as well harnessing available patient-specific data greatest extent possible. Although researchers beginning realize that taking uncertainty model input parameters into account necessity, predominantly probabilistic description for these uncertain based on elementary random...
The aim of this paper is to give an overview different multifidelity uncertainty quantification (UQ) schemes. Therefore, views on UQ approaches from a frequentist, Bayesian, and possibilistic perspective are provided recent developments discussed. Differences as well similarities between the methods highlighted strategies construct low‐fidelity models explained. In addition, two state‐of‐the‐art examples showcase capabilities these tremendous reduction computational costs that can be...
The big crux with drug delivery to human lungs is that the delivered dose at local site of action unpredictable and very difficult measure, even a posteriori. It highly subject-specific as it depends on lung morphology, disease, breathing, aerosol characteristics. Given these challenges, computational approaches have shown potential, but so far failed due fundamental methodical limitations. We present validate novel in silico model enables prediction deposition throughout entire lung. Its...
Two of the most significant challenges in uncertainty quantification pertain to high computational cost for simulating complex physical models and dimension random inputs. In applications practical interest, both these problems are encountered, standard methods either fail or not feasible. To overcome current limitations, we present a generalized formulation Bayesian multi-fidelity Monte-Carlo (BMFMC) framework that can exploit lower-fidelity model versions small data regime. The goal our...
In this work, we predict the outcomes of high fidelity multivariate computer simulations from low counterparts using function-to-function regression. The simulation takes place on a definition mesh, while its counterpart coarsened and truncated mesh. We showcase our approach by applying it to complex finite element an abdominal aortic aneurysm which provides displacement field blood vessel under pressure. order link two multidimensional compress them then fit regression model. data are...
Modeling of mechanical systems with uncertainties is extremely challenging and requires a careful analysis huge amount data. Both, probabilistic modeling nonprobabilistic require either an large ensemble samples or the introduction additional dimensions to problem, thus, resulting also in enormous computational cost growth. No matter whether Monte‐Carlo sampling Smolyak's sparse grids are used, which may theoretically overcome curse dimensionality, system evaluation must be performed at...
Boundary condition (BC) calibration to assimilate clinical measurements is an essential step in any subject-specific simulation of cardiovascular fluid dynamics. Bayesian approaches have successfully quantified the uncertainties inherent identified parameters. Yet, routinely estimating posterior distribution for all BC parameters 3D simulations has been unattainable due infeasible computational demand. We propose efficient method identify Windkessel parameter posteriors using results from a...
The choice of lung protective ventilation settings for mechanical has a considerable impact on patient outcome, yet identifying optimal ventilatory individual patients remains highly challenging due to the inherent inter- and intra-patient pathophysiological variability. In this validation study, we demonstrate that physics-based computational models tailored can resolve variability, allowing us predict otherwise unknown local state pathologically affected during ventilation. For seven ARDS...
In this work we propose a low rank approximation of high fidelity finite element simulations by utilizing weights corresponding to areas stress levels for an abdominal aortic aneurysm, i.e. deformed blood vessel. We focus on the van Mises stress, which corresponds rupture risk aorta. This is modeled as Gaussian Markov random field and define our basis vectors that solve series optimization problems. Each these problems describes minimization expected weighted quadratic loss. The weights,...