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Lavigne T.♦, Bordas S.♦, Lengiewicz J., Identification of material parameters and traction field for soft bodies in contact,
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, ISSN: 0045-7825, DOI: 10.1016/j.cma.2023.115889, Vol.406, No.115889, pp.1-22, 2023Abstract: We provide an optimization framework that is capable of identifying the material parameters and contact traction field from two measured deformed geometries of a soft body in contact. The novelty of the framework is the idea of parametrizing the missing contact traction field and incorporating it into the inverse+forward hyper-elasticity formulation. We provide the continuum- and finite element formulation of the framework, as well as the direct differentiation method of sensitivity analysis to efficiently obtain necessary gradients for the BFGS optimizer. The correctness of the formulation and the excellent performance of the framework are confirmed by a series of benchmark numerical examples. Keywords: Hyper-elasticity, Inverse form, Large strains, Contact, Calibration, Soft bodies Affiliations:
Lavigne T. | - | other affiliation | Bordas S. | - | University of Luxembourg (LU) | Lengiewicz J. | - | IPPT PAN |
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Deshpande S.♦, Sosa R.♦, Bordas S.P.♦, Lengiewicz J.A., Convolution, aggregation and attention based deep neural networks for accelerating simulations in mechanics,
Frontiers in Materials, ISSN: 2296-8016, DOI: 10.3389/fmats.2023.1128954, Vol.10, No.1128954, pp.1-12, 2023Abstract: Deep learning surrogate models are being increasingly used in accelerating scientific simulations as a replacement for costly conventional numerical techniques. However, their use remains a significant challenge when dealing with real-world complex examples. In this work, we demonstrate three types of neural network architectures for efficient learning of highly non-linear deformations of solid bodies. The first two architectures are based on the recently proposed CNN U-NET and MAgNET (graph U-NET) frameworks which have shown promising performance for learning on mesh-based data. The third architecture is Perceiver IO, a very recent architecture that belongs to the family of attention-based neural networks–a class that has revolutionised diverse engineering fields and is still unexplored in computational mechanics. We study and compare the performance of all three networks on two benchmark examples, and show their capabilities to accurately predict the non-linear mechanical responses of soft bodies. Keywords: surrogate modeling, deep learning-artificial neural network, CNN U-NET, graph U-net, perceiver IO, finite element method Affiliations:
Deshpande S. | - | University of Luxembourg (LU) | Sosa R. | - | other affiliation | Bordas S.P. | - | University of Luxembourg (LU) | Lengiewicz J.A. | - | IPPT PAN |
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Deshpande S.♦, Lengiewicz J., Bordas S.P.A.♦, Probabilistic deep learning for real-time large deformation simulations,
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, ISSN: 0045-7825, DOI: 10.1016/j.cma.2022.115307, Vol.398, pp.115307-1-115307-26, 2022Abstract: For many novel applications, such as patient-specific computer-aided surgery, conventional solution techniques of the underlying nonlinear problems are usually computationally too expensive and are lacking information about how certain can we be about their predictions. In the present work, we propose a highly efficient deep-learning surrogate framework that is able to accurately predict the response of bodies undergoing large deformations in real-time. The surrogate model has a convolutional neural network architecture, called U-Net, which is trained with force–displacement data obtained with the finite element method. We propose deterministic and probabilistic versions of the framework. The probabilistic framework utilizes the Variational Bayes Inference approach and is able to capture all the uncertainties present in the data as well as in the deep-learning model. Based on several benchmark examples, we show the predictive capabilities of the framework and discuss its possible limitations. Keywords: convolutional neural network, Bayesian inference, Bayesian deep learning, large deformations, finite element method, real-time simulations Affiliations:
Deshpande S. | - | University of Luxembourg (LU) | Lengiewicz J. | - | IPPT PAN | Bordas S.P.A. | - | University of Luxembourg (LU) |
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Lavigne T.♦, Mazier A.♦, Perney A.♦, Bordas S.P.A.♦, Hild F.♦, Lengiewicz J., Digital Volume Correlation for large deformations of soft tissues: Pipeline and proof of concept for the application to breast ex vivo deformations,
Journal of the Mechanical Behavior of Biomedical Materials, ISSN: 1751-6161, DOI: 10.1016/j.jmbbm.2022.105490, Vol.136, No.105490, pp.1-13, 2022Abstract: Being able to reposition tumors from prone imaging to supine surgery stances is key for bypassing current invasive marking used for conservative breast surgery. This study aims to demonstrate the feasibility of using Digital Volume Correlation (DVC) to measure the deformation of a female quarter thorax between two different body positioning when subjected to gravity. A segmented multipart mesh (bones, cartilage and tissue) was constructed and a three-step FE-based DVC procedure with heterogeneous elastic regularization was implemented. With the proposed framework, the large displacement field of a hard/soft breast sample was recovered with low registration residuals and small error between the measured and manually determined deformations of phase interfaces. The present study showed the capacity of FE-based DVC to faithfully capture large deformations of hard/soft tissues. Keywords: Digital Volume Correlation, Elastic regularization, Hard/soft tissues, Large displacements, Kinematic fields, X-ray tomography Affiliations:
Lavigne T. | - | other affiliation | Mazier A. | - | other affiliation | Perney A. | - | other affiliation | Bordas S.P.A. | - | University of Luxembourg (LU) | Hild F. | - | other affiliation | Lengiewicz J. | - | IPPT PAN |
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Piranda B.♦, Chodkiewicz P.♦, Hołobut P., Bordas S.P.A.♦, Bourgeois J.♦, Lengiewicz J., Distributed prediction of unsafe reconfiguration scenarios of modular robotic programmable matter,
IEEE TRANSACTIONS ON ROBOTICS, ISSN: 1552-3098, DOI: 10.1109/TRO.2021.3074085, Vol.37, No.6, pp.2226-2233, 2021Abstract: We present a distributed framework for predicting whether a planned reconfiguration step of a modular robot will mechanically overload the structure, causing it to break or lose stability under its own weight. The algorithm is executed by the modular robot itself and based on a distributed iterative solution of mechanical equilibrium equations derived from a simplified model of the robot. The model treats intermodular connections as beams and assumes no-sliding contact between the modules and the ground. We also provide a procedure for simplified instability detection. The algorithm is verified in the Programmable Matter simulator VisibleSim, and in real-life experiments on the modular robotic system Blinky Blocks. Keywords: distributed algorithms, modular robots, mechanical constraints, programmable matter, self-reconfiguration Affiliations:
Piranda B. | - | other affiliation | Chodkiewicz P. | - | Warsaw University of Technology (PL) | Hołobut P. | - | IPPT PAN | Bordas S.P.A. | - | University of Luxembourg (LU) | Bourgeois J. | - | other affiliation | Lengiewicz J. | - | IPPT PAN |
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