Browsing by Author "Schulte, Miriam"

Now showing 1 - 6 of 6

Open Access
CLAIRE : parallelized diffeomorphic image registration for large-scale biomedical imaging applications
(2022) Himthani, Naveen; Brunn, Malte; Kim, Jae-Youn; Schulte, Miriam; Mang, Andreas; Biros, George
We study the performance of CLAIRE - a diffeomorphic multi-node, multi-GPU image-registration algorithm and software-in large-scale biomedical imaging applications with billions of voxels. At such resolutions, most existing software packages for diffeomorphic image registration are prohibitively expensive. As a result, practitioners first significantly downsample the original images and then register them using existing tools. Our main contribution is an extensive analysis of the impact of downsampling on registration performance. We study this impact by comparing full-resolution registrations obtained with CLAIRE to lower resolution registrations for synthetic and real-world imaging datasets. Our results suggest that registration at full resolution can yield a superior registration quality-but not always. For example, downsampling a synthetic image from 10243 to 2563 decreases the Dice coefficient from 92% to 79%. However, the differences are less pronounced for noisy or low contrast high resolution images. CLAIRE allows us not only to register images of clinically relevant size in a few seconds but also to register images at unprecedented resolution in reasonable time. The highest resolution considered are CLARITY images of size 2816×3016×1162. To the best of our knowledge, this is the first study on image registration quality at such resolutions.
Open Access
Coupled simulations and parameter inversion for neural system and electrophysiological muscle models
(2024) Homs‐Pons, Carme; Lautenschlager, Robin; Schmid, Laura; Ernst, Jennifer; Göddeke, Dominik; Röhrle, Oliver; Schulte, Miriam
The functioning of the neuromuscular system is an important factor for quality of life. With the aim of restoring neuromuscular function after limb amputation, novel clinical techniques such as the agonist‐antagonist myoneural interface (AMI) are being developed. In this technique, the residual muscles of an agonist‐antagonist pair are (re‐)connected via a tendon in order to restore their mechanical and neural interaction. Due to the complexity of the system, the AMI can substantially profit from in silico analysis, in particular to determine the prestretch of the residual muscles that is applied during the procedure and determines the range of motion of the residual muscle pair. We present our computational approach to facilitate this. We extend a detailed multi‐X model for single muscles to the AMI setup, that is, a two‐muscle‐one‐tendon system. The model considers subcellular processes as well as 3D muscle and tendon mechanics and is prepared for neural process simulation. It is solved on high performance computing systems. We present simulation results that show (i) the performance of our numerical coupling between muscles and tendon and (ii) a qualitatively correct dependence of the range of motion of muscles on their prestretch. Simultaneously, we pursue a Bayesian parameter inference approach to invert for parameters of interest. Our approach is independent of the underlying muscle model and represents a first step toward parameter optimization, for instance, finding the prestretch, to be applied during surgery, that maximizes the resulting range of motion. Since our multi‐X fine‐grained model is computationally expensive, we present inversion results for reduced Hill‐type models. Our numerical results for cases with known ground truth show the convergence and robustness of our approach.
Open Access
Efficient and scalable initialization of partitioned coupled simulations with preCICE
(2021) Totounferoush, Amin; Simonis, Frédéric; Uekermann, Benjamin; Schulte, Miriam
preCICE is an open-source library, that provides comprehensive functionality to couple independent parallelized solver codes to establish a partitioned multi-physics multi-code simulation environment. For data communication between the respective executables at runtime, it implements a peer-to-peer concept, which renders the computational cost of the coupling per time step negligible compared to the typical run time of the coupled codes. To initialize the peer-to-peer coupling, the mesh partitions of the respective solvers need to be compared to determine the point-to-point communication channels between the processes of both codes. This initialization effort can become a limiting factor, if we either reach memory limits or if we have to re-initialize communication relations in every time step. In this contribution, we remove two remaining bottlenecks: (i) We base the neighborhood search between mesh entities of two solvers on a tree data structure to avoid quadratic complexity, and (ii) we replace the sequential gather-scatter comparison of both mesh partitions by a two-level approach that first compares bounding boxes around mesh partitions in a sequential manner, subsequently establishes pairwise communication between processes of the two solvers, and finally compares mesh partitions between connected processes in parallel. We show, that the two-level initialization method is fives times faster than the old one-level scheme on 24,567 CPU-cores using a mesh with 628,898 vertices. In addition, the two-level scheme is able to handle much larger computational meshes, since the central mesh communication of the one-level scheme is replaced with a fully point-to-point mesh communication scheme.
Open Access
Enhancing quasi-newton acceleration for fluid-structure interaction
(2022) Davis, Kyle; Schulte, Miriam; Uekermann, Benjamin
We propose two enhancements of quasi-Newton methods used to accelerate coupling iterations for partitioned fluid-structure interaction. Quasi-Newton methods have been established as flexible, yet robust, efficient and accurate coupling methods of multi-physics simulations in general. The coupling library preCICE provides several variants, the so-called IQN-ILS method being the most commonly used. It uses input and output differences of the coupled solvers collected in previous iterations and time steps to approximate Newton iterations. To make quasi-Newton methods both applicable for parallel coupling (where these differences contain data from different physical fields) and to provide a robust approach for re-using information, a combination of information filtering and scaling for the different physical fields is typically required. This leads to good convergence, but increases the cost per iteration. We propose two new approaches - pre-scaling weight monitoring and a new, so-called QR3 filter, to substantially improve runtime while not affecting convergence quality. We evaluate these for a variety of fluid-structure interaction examples. Results show that we achieve drastic speedups for the pure quasi-Newton update steps. In the future, we intend to apply the methods also to volume-coupled scenarios, where these gains can be decisive for the feasibility of the coupling approach.
Open Access
Knowledge-based modeling of simulation behavior for Bayesian optimization
(2024) Huber, Felix; Bürkner, Paul-Christian; Göddeke, Dominik; Schulte, Miriam
Numerical simulations consist of many components that affect the simulation accuracy and the required computational resources. However, finding an optimal combination of components and their parameters under constraints can be a difficult, time-consuming and often manual process. Classical adaptivity does not fully solve the problem, as it comes with significant implementation cost and is difficult to expand to multi-dimensional parameter spaces. Also, many existing data-based optimization approaches treat the optimization problem as a black-box, thus requiring a large amount of data. We present a constrained, model-based Bayesian optimization approach that avoids black-box models by leveraging existing knowledge about the simulation components and properties of the simulation behavior. The main focus of this paper is on the stochastic modeling ansatz for simulation error and run time as optimization objective and constraint, respectively. To account for data covering multiple orders of magnitude, our approach operates on a logarithmic scale. The models use a priori knowledge of the simulation components such as convergence orders and run time estimates. Together with suitable priors for the model parameters, the model is able to make accurate predictions of the simulation behavior. Reliably modeling the simulation behavior yields a fast optimization procedure because it enables the optimizer to quickly indicate promising parameter values. We test our approach experimentally using the multi-scale muscle simulation framework OpenDiHu and show that we successfully optimize the time step widths in a time splitting approach in terms of minimizing the overall error under run time constraints.
Open Access
Simulation of flow in deformable fractures using a quasi-Newton based partitioned coupling approach
(2022) Schmidt, Patrick; Jaust, Alexander; Steeb, Holger; Schulte, Miriam
We introduce a partitioned coupling approach for iterative coupling of flow processes in deformable fractures embedded in a poro-elastic medium that is enhanced by interface quasi-Newton (IQN) methods. In this scope, a unique computational decomposition into a fracture flow and a poro-elastic domain is developed, where communication and numerical coupling of the individual solvers are realized by consulting the open-source library preCICE. The underlying physical problem is introduced by a brief derivation of the governing equations and interface conditions of fracture flow and poro-elastic domain followed by a detailed discussion of the partitioned coupling scheme. We evaluate the proposed implementation and undertake a convergence study to compare a classical interface quasi-Newton inverse least-squares (IQN-ILS) with the more advanced interface quasi-Newton inverse multi-vector Jacobian (IQN-IMVJ) method. These coupling approaches are verified for an academic test case before the generality of the proposed strategy is demonstrated by simulations of two complex fracture networks. In contrast to the development of specific solvers, we promote the simplicity and computational efficiency of the proposed partitioned coupling approach using preCICE and FEniCS for parallel computations of hydro-mechanical processes in complex, three-dimensional fracture networks.