10th edition of the International Conference on Computational Methods for Coupled Problems in Science and Engineering (COUPLED PROBLEMS 2023)

The objectives of COUPLED PROBLEMS 2023 are to present and discuss state of the art, mathematical models, numerical methods and computational techniques for solving coupling problems of multidisciplinary character in science and engineering. The conference goal is to make step forward in the formulation and solution of real life problems with a multidisciplinary vision, accounting for all the complex couplings involved in the physical description of the problem.

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Plenary Lectures

Multiphysical modeling of soft-tissue stent interaction (Plenary Lecture)
S. Reese, K. Manjunatha, J. Shi, M. Sesa, M. Behr, F. Vogt

coupled2023.

Abstract
The efficacy of cardiological interventions, including the implantation of prostheses, is highly dependent on patient-specific immunohistology and can be enhanced with computational predictive tools. Therefore, an in silico replication of neointimal hyperplasia shall provide the necessary insights about the biochemical and cellular interactions within the vessel wall, and eventually address the risks of in-stent restenosis in a patient-specific manner. In this context, we set up a multiphysics framework considering key mediators of restenosis and couple them to a continuum mechanical vessel wall model. The governing set of coupled partial PDEs for the underlying mechanobiological system is solved via the finite element method and the results are compared to those obtained using a deep learning framework employing physics-informed neural networks (PINNs). Another interesting cardiological intervention-related problem is the maturation of tissue-engineered cardiovascular implants wherein the evolution of the collagen density affects the tissue’s mechanical behavior. The model we present allows us to predict the evolution of collagen density within textile-reinforced heart valves.
Abstract
The efficacy of cardiological interventions, including the implantation of prostheses, is highly dependent on patient-specific immunohistology and can be enhanced with computational [...]
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A coupled model for the formation of Atherosclerosis due to inflammation processes
P. Wriggers, M. Gierig, A. Haverich

coupled2023.

Abstract
Atherosclerosis is a disease in blood vessels that often results in plaque formation and lumen narrowing. It is an inflammatory response of the tissue caused by disruptions in the vessel wall nourishment. Blood vessels are nourished by nutrients originating from the blood of the lumen. In medium-sized and larger vessels, nutrients are additionally provided from outside through a network of capillaries called vasa vasorum. It has recently been hypothesized [1] that the root of atherosclerotic diseases is the malfunction of the vasa vasorum. This, so called outside-in-theory, is supported by a recently developed numerical model [2] accounting for the inflammation initiation in the adventitial layer of the blood vessel. Building on the previous findings, this presentation proposes an extended material model for atherosclerosis formation that is based on the outside-in-theory. Beside the description of growth kinematics and nutrient diffusion, the roles of monocytes, macrophages, foam cells, smooth muscle cells and collagen are accounted for in a nonlinear continuum mechanics setting. Cells are activated due to a lack of vessel wall nourishment and proliferate, migrate, differentiate and synthesize collagen, leading to the formation of a plaque. Numerical studies show that the onset of atherosclerosis can qualitatively be reproduced. Thus, the in silico model backs the new theory.
Abstract
Atherosclerosis is a disease in blood vessels that often results in plaque formation and lumen narrowing. It is an inflammatory response of the tissue caused by disruptions [...]
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Reduced-Order Models in Bayesian solvers for inverse problems
P. Diez, S. Zlotnik, A. Muixí, A. García-González

coupled2023.

Abstract
Challenging inverse problems aim at identifying large sets of parameters using data from different sources and diverse accuracy. This is the case of data assimilation for geophysical crust dynamics, were the number of parameters to identify amounts to thousands. In this context, Bayesian inverse solvers combined with Markov-Chain Monte Carlo (MCMC) strategies are an affordable strategy, accounting for the uncertainty of the input data and quantifying also uncertainty of the output. Despite the efficiency of the MCMC approach, the direct problem has to be evaluated an extremely large number of times, many (after the burn-in phase) with the input parameters lying in a narrow range. This is the ideal situation for Reduced-Order Models (ROM): many repeated queries to the model corresponding to parameters lying in a limited manifold. Thus, we aim at applying ROM to large-dimensional parametric forward problems. In this case, it is important optimising the dimensionality reduction technique inherent to the ROM strategy. For instance, Proper Orthogonal Decomposition (POD) is associated with a linear Principal Component Analysis (PCA). PCA is linear in the sense that assumes the reduceddimension manifold to be Euclidean. We explore using kernel PCA (kPCA) to further reduce the dimension, thus devising a kPOD approach. Different options to select physically inspired kernels, based on the knowledge of the problem under consideration, are discussed. Moreover, the computational strategy to explore the feature space (the reduced-dimensional space) is also discussed.
Abstract
Challenging inverse problems aim at identifying large sets of parameters using data from different sources and diverse accuracy. This is the case of data assimilation for [...]
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Model Order Reduction in the Parametrized Multi-Scale and Coupled Setting
K. Veroy-Grepl

coupled2023.
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A New Paradigm for Multiphysics Simulation: Its Initial Application to Fluid-Structure Interaction
K. Park, J. González

coupled2023.

Abstract
A new paradigm for simulating mulitipysics systems is presented with initial focus on FluidStructure Interaction (FSI). For each single-field governing equations adopting Lagrangian frame, a projection operator couples the coupled field without invoking interface Lagrange multipliers. For FSI problems where fluids are modeled by employing ALE kinematics, a physics-based interface equations are formulated as an independent set of third-field equations. This approach facilitates the connection of non-matching meshes and provides consistent dynamic equations of motion for the interface that can be integrated in parallel. The proposed simulation paradigm adopts stand-alone software modules for the fluid and the structure, which is coupled through a third interface system treating their interaction, which preserves the modularity of the singlediscipline software modules. The new FSI simulation paradigm is demonstrated as applied to several FSI benchmark examples, demonstrating its efficiency and accuracy. Extension of the proposed new multiphysics simulation paradigm to treat other multiplhysics problems are suggested.
Abstract
A new paradigm for simulating mulitipysics systems is presented with initial focus on FluidStructure Interaction (FSI). For each single-field governing equations adopting [...]
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Coupled problems in bio-inspired robotics
A. Simone

coupled2023.
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Advances in analysis, algorithms, and software for the coupling of conventional and data-driven models for heterogeneous multi-scale, multi-physics simulations

Advances in Multiphysics Modelling and Simulation of Electromagnetic Systems

Coupled problems with geometric reduction methods

Coupling image processing and computational modeling for biomedical applications

Flow-Structure Interaction in Bio-Inspired Locomotion/Transport Problems: Methods and Applications

Interdisciplinary Alliance in Biosciences: From physics-basedanddata-driven multiscale modelling to medical applications

Iterative Methods and Preconditioners for Challenging Multiphysics Systems

Machine learning and uncertainty quantification for coupled multi-physics, multi-scale and multi-fidelity modelling.

Multi-Physics and Multi-Scale Simulations with the Coupling Library preCICE

Multiscale and coupled problems in bioengineering

Nonlinear and deep learning based model reduction for coupled problems

Particle-based methods in Coupled Problems: advances and applications in DEM, PFEM, SPH, MPM, MPS and others

Progress in Computational Multiphysics Using Open-source Software

Quasi-Newton techniques for partitioned simulation of coupled problems

Recent trends in model order reduction for coupled problems

Sharing Advances in Modelling Techniques for Fluid-Structure Interaction

Simulation methods for coupled problems

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Plenary Lectures

Multiphysical modeling of soft-tissue stent interaction (Plenary Lecture)

A coupled model for the formation of Atherosclerosis due to inflammation processes

Reduced-Order Models in Bayesian solvers for inverse problems

Model Order Reduction in the Parametrized Multi-Scale and Coupled Setting

A New Paradigm for Multiphysics Simulation: Its Initial Application to Fluid-Structure Interaction

Coupled problems in bio-inspired robotics