arXiv subjects

Jonas Biehler

Publications and source records attributed to Jonas Biehler.

6 recordsLinked to original sources

arXiv2025

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models

A growing challenge in research and industrial engineering applications is the need for repeated, systematic analysis of large-scale computational models, for example, patient-specific digital twins of diseased human organs: The analysis requires efficient implementation, data, resource management, and parallelization, possibly on distributed systems. To tackle these challenges and save many researchers from annoying, time-consuming tasks, we present QUEENS (Quantification of Uncertain Effects in Engineering Systems), an open-source Python framework for composing and managing simulation analyses with arbitrary (physics-based) solvers on distributed computing infrastructures. Besides simulation management capabilities, QUEENS offers a comprehensive collection of efficiently implemented state-of-the-art algorithms ranging from routines for convergence studies and common optimization algorithms to more advanced sampling algorithms for uncertainty quantification and Bayesian inverse analysis. Additionally, we provide our latest cutting-edge research in multi-fidelity uncertainty quantification, efficient multi-fidelity Bayesian inverse analysis, and probabilistic machine learning. QUEENS adopts a Bayesian, probabilistic mindset but equally supports standard deterministic analysis without requiring prior knowledge of probability theory. The modular architecture allows rapid switching between common types of analyses and facilitates building sophisticated hierarchical algorithms. Encouraging natural incremental steps and scaling towards complexity allows researchers to consider the big picture while building towards it through smaller, manageable steps. The open-source repository is available at https://github.com/queens-py/queens.

cs.CE↗

arXiv2024

Patient-specific prediction of regional lung mechanics in ARDS patients with physics-based models: a validation study

The choice of lung protective ventilation settings for mechanical ventilation has a considerable impact on patient outcome, yet identifying optimal ventilatory settings for 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 lung models tailored to individual patients can resolve this variability, allowing us to predict the otherwise unknown local state of the pathologically affected lung during mechanical ventilation. For seven ARDS patients undergoing invasive mechanical ventilation, physics-based, patient-specific lung models were created using chest CT scans and ventilatory data. By numerically resolving the interaction of the pathological lung with the airway pressure and flow imparted by the ventilator, we predict the time-dependent and heterogeneous local state of the lung for each patient and compare it against the regional ventilation obtained from bedside monitoring using Electrical Impedance Tomography. Excellent agreement between numerical simulations and experimental data was obtained, with the model-predicted anteroposterior ventilation profile achieving a Pearson correlation of 96% with the clinical reference data. Even when considering the regional ventilation within the entire transverse chest cross-section and across the entire dynamic ventilation range, an average correlation of more than 81% and an average root mean square error of less than 15% were achieved. The results of this first systematic validation study demonstrate the ability of computational models to provide clinically relevant information and thereby open the door for a truly patient-specific choice of ventilator settings on the basis of both individual anatomy and pathophysiology.

physics.med-ph↗

arXiv2024

Bayesian Windkessel calibration using optimized 0D surrogate models

Boundary condition (BC) calibration to assimilate clinical measurements is an essential step in any subject-specific simulation of cardiovascular fluid dynamics. Bayesian calibration approaches have successfully quantified the uncertainties inherent in identified parameters. Yet, routinely estimating the posterior distribution for all BC parameters in 3D simulations has been unattainable due to the infeasible computational demand. We propose an efficient method to identify Windkessel parameter posteriors using results from a single high-fidelity three-dimensional (3D) model evaluation. We only evaluate the 3D model once for an initial choice of BCs and use the result to create a highly accurate zero-dimensional (0D) surrogate. We then perform Sequential Monte Carlo (SMC) using the optimized 0D model to derive the high-dimensional Windkessel BC posterior distribution. We validate this approach in a publicly available dataset of N=72 subject-specific vascular models. We found that optimizing 0D models to match 3D data a priori lowered their median approximation error by nearly one order of magnitude. In a subset of models, we confirm that the optimized 0D models still generalize to a wide range of BCs. Finally, we present the high-dimensional Windkessel parameter posterior for different measured signal-to-noise ratios in a vascular model using SMC. We further validate that the 0D-derived posterior is a good approximation of the 3D posterior. The minimal computational demand of our method using a single 3D simulation, combined with the open-source nature of all software and data used in this work, will increase access and efficiency of Bayesian Windkessel calibration in cardiovascular fluid dynamics simulations.

cs.CE↗

arXiv2023

In silico high-resolution whole lung model to predict the locally delivered dose of inhaled drugs

The big crux with drug delivery to human lungs is that the delivered dose at the local site of action is unpredictable and very difficult to measure, even a posteriori. It is highly subject-specific as it depends on lung morphology, disease, breathing, and aerosol characteristics. Given these challenges, computational approaches have shown potential, but have so far failed due to fundamental methodical limitations. We present and validate a novel in silico model that enables the subject-specific prediction of local aerosol deposition throughout the entire lung. Its unprecedented spatiotemporal resolution allows to track each aerosol particle anytime during the breathing cycle, anywhere in the complete system of conducting airways and the alveolar region. Predictions are shown to be in excellent agreement with in vivo SPECT/CT data for a healthy human cohort. We further showcase the model's capabilities to represent strong heterogeneities in diseased lungs by studying an IPF patient. Finally, high computational efficiency and automated model generation and calibration ensure readiness to be applied at scale. We envision our method not only to improve inhalation therapies by informing and accelerating all stages of (pre-)clinical drug and device development, but also as a more-than-equivalent alternative to nuclear imaging of the lungs.

physics.med-ph↗

arXiv2023

Weighted high dimensional data reduction of finite Element Features -- An Application on High Pressure of an Abdominal Aortic Aneurysm

In this work we propose a low rank approximation of high fidelity finite element simulations by utilizing weights corresponding to areas of high stress levels for an abdominal aortic aneurysm, i.e. a deformed blood vessel. We focus on the van Mises stress, which corresponds to the rupture risk of the aorta. This is modeled as a Gaussian Markov random field and we define our approximation as a basis of vectors that solve a series of optimization problems. Each of these problems describes the minimization of an expected weighted quadratic loss. The weights, which encapsulate the importance of each grid point of the finite elements, can be chosen freely - either data driven or by incorporating domain knowledge. Along with a more general discussion of mathematical properties we provide an effective numerical heuristic to compute the basis under general conditions. We explicitly explore two such bases on the surface of a high fidelity finite element grid and show their efficiency for compression. We further utilize the approach to predict the van Mises stress in areas of interest using low and high fidelity simulations. Due to the high dimension of the data we have to take extra care to keep the problem numerically feasible. This is also a major concern of this work.

math.NA↗

arXiv2022

A Generalized Probabilistic Learning Approach for Multi-Fidelity Uncertainty Propagation in Complex Physical Simulations

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 random inputs. In applications of practical interest, both of these problems are encountered, and standard methods either fail or are not feasible. To overcome the current limitations, we present a generalized formulation of a Bayesian multi-fidelity Monte-Carlo (BMFMC) framework that can exploit lower-fidelity model versions in a small data regime. The goal of our analysis is an efficient and accurate estimation of the complete probabilistic response for high-fidelity models. BMFMC circumvents the curse of dimensionality by learning the relationship between the outputs of a reference high-fidelity model and potentially several lower-fidelity models. While the continuous formulation is mathematically exact and independent of the low-fidelity model's accuracy, we address challenges associated with the small data regime (i.e., only a small number of 50 to 300 high-fidelity model runs can be performed). Specifically, we complement the formulation with a set of informative input features at no extra cost. Despite the inaccurate and noisy information that some low-fidelity models provide, we demonstrate that accurate and certifiable estimates for the quantities of interest can be obtained for uncertainty quantification problems in high stochastic dimensions, with significantly fewer high-fidelity model runs than state-of-the-art methods for uncertainty quantification. We illustrate our approach by applying it to challenging numerical examples such as Navier-Stokes flow simulations and fluid-structure interaction problems.

cs.CE↗