Imaging Informatics and the Human Brain Project: the Role of Structure, Review

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Quasi-Monolithic Graph Neural Network for Fluid-Structure Interaction

Using convolutional neural networks, deep learning-based reduced-order models have demonstrated great potential in accelerating the simulations of coupled fluid-structure systems for downstream optimization and control tasks. However, these networks have to operate on a uniform Cartesian grid due to the inherent restriction of convolutions, leading to difficulties in extracting fine physical details along a fluid-structure interface without excessive computational burden. In this work, we present a quasi-monolithic graph neural network framework for the reduced-order modelling of fluid-structure interaction systems. With the aid of an arbitrary Lagrangian-Eulerian formulation, the mesh and fluid states are evolved temporally with two sub-networks. The movement of the mesh is reduced to the evolution of several coefficients via proper orthogonal decomposition, and these coefficients are propagated through time via a multi-layer perceptron. A graph neural network is employed to predict the evolution of the fluid state based on the state of the whole system. The structural state is implicitly modelled by the movement of the mesh on the fluid-structure boundary; hence it makes the proposed data-driven methodology quasi-monolithic. The effectiveness of the proposed quasi-monolithic graph neural network architecture is assessed on a prototypical fluid-structure system of the flow around an elastically-mounted cylinder. We use the full-order flow snapshots and displacements as target physical data to learn and infer coupled fluid-structure dynamics. The proposed framework tracks the interface description and provides the state predictions during roll-out with acceptable accuracy. We also directly extract the lift and drag forces from the predicted fluid and mesh states, in contrast to existing convolution-based architectures

Survey of Finite Element Method-Based Real-Time Simulations

The finite element method (FEM) has deservedly gained the reputation of the most powerful, highly efficient, and versatile numerical method in the field of structural analysis. Though typical application of FE programs implies the so-called “off-line” computations, the rapid pace of hardware development over the past couple of decades was the major impetus for numerous researchers to consider the possibility of real-time simulation based on FE models. Limitations of available hardware components in various phases of developments demanded remarkable innovativeness in the quest for suitable solutions to the challenge. Different approaches have been proposed depending on the demands of the specific field of application. Though it is still a relatively young field of work in global terms, an immense amount of work has already been done calling for a representative survey. This paper aims to provide such a survey, which of course cannot be exhaustive

Mindboggle: Automated brain labeling with multiple atlases

BACKGROUND: To make inferences about brain structures or activity across multiple individuals, one first needs to determine the structural correspondences across their image data. We have recently developed Mindboggle as a fully automated, feature-matching approach to assign anatomical labels to cortical structures and activity in human brain MRI data. Label assignment is based on structural correspondences between labeled atlases and unlabeled image data, where an atlas consists of a set of labels manually assigned to a single brain image. In the present work, we study the influence of using variable numbers of individual atlases to nonlinearly label human brain image data. METHODS: Each brain image voxel of each of 20 human subjects is assigned a label by each of the remaining 19 atlases using Mindboggle. The most common label is selected and is given a confidence rating based on the number of atlases that assigned that label. The automatically assigned labels for each subject brain are compared with the manual labels for that subject (its atlas). Unlike recent approaches that transform subject data to a labeled, probabilistic atlas space (constructed from a database of atlases), Mindboggle labels a subject by each atlas in a database independently. RESULTS: When Mindboggle labels a human subject's brain image with at least four atlases, the resulting label agreement with coregistered manual labels is significantly higher than when only a single atlas is used. Different numbers of atlases provide significantly higher label agreements for individual brain regions. CONCLUSION: Increasing the number of reference brains used to automatically label a human subject brain improves labeling accuracy with respect to manually assigned labels. Mindboggle software can provide confidence measures for labels based on probabilistic assignment of labels and could be applied to large databases of brain images

Piecewise Affine Registration of Biological Images for Volume Reconstruction

This manuscript tackles the reconstruction of 3D volumes via mono-modal registration of series of 2D biological images (histological sections, autoradiographs, cryosections, etc.). The process of acquiring these images typically induces composite transformations that we model as a number of rigid or affine local transformations embedded in an elastic one. We propose a registration approach closely derived from this model. Given a pair of input images, we first compute a dense similarity field between them with a block matching algorithm. We use as a similarity measure an extension of the classical correlation coefficient that improves the consistency of the field. A hierarchical clustering algorithm then automatically partitions the field into a number of classes from which we extract independent pairs of sub-images. Our clustering algorithm relies on the Earth mover’s distribution metric and is additionally guided by robust least-square estimation of the transformations associated with each cluster. Finally, the pairs of sub-images are, independently, affinely registered and a hybrid affine/non-linear interpolation scheme is used to compose the output registered image. We investigate the behavior of our approach on several batches of histological data and discuss its sensitivity to parameters and noise

Efficient Motion Planning for Deformable Objects with High Degrees of Freedom

Many robotics and graphics applications need to be able to plan motions by interacting with complex environmental objects, including solids, sands, plants, and fluids. A key aspect of these deformable objects is that they have high-DOF, which implies that they can move or change shapes in many independent ways subject to physics-based constraints. In these applications, users also impose high-level goals on the movements of high-DOF objects, and planning algorithms need to model their motions and determine the optimal control actions to satisfy the high-level goals. In this thesis, we propose several planning algorithms for high-DOF objects. Our algorithms can improve the scalability considerably and can plan motions for different types of objects, including elastically deformable objects, free-surface flows, and Eulerian fluids. We show that the salient deformations of elastically deformable objects lie in a low-dimensional nonlinear space, i.e., the RS space. By embedding the configuration space in the RS subspace, our optimization-based motion planning algorithm can achieve over two orders of magnitude speedup over prior optimization-based formulations. For free surface flows such as liquids, we utilize features of the planning problems and machine learning techniques to identify low-dimensional latent spaces to accelerate the motion planning computation. For Eulerian fluids without free surfaces, we present a scalable planning algorithm based on novel numerical techniques. We show that the numerical discretization scheme exhibits strong regularity, which allows us to accelerate optimization-based motion planning algorithms using a hierarchical data structure and we can achieve 3-10 times speedup over gradient-based optimization techniques. Finally, for high-DOF objects with many frictional contacts with the environment, we present a contact dynamic model that can handle contacts without expensive combinatorial optimization. We illustrate the benefits of our high-DOF planning algorithms for three applications. First, we can plan contact-rich motion trajectories for general elastically deformable robots. Second, we can achieve real-time performance in terms of planning the motion of a robot arm to transfer the liquids between containers. Finally, our method enables a more intuitive user interface. We allow animation editors to modify animations using an offline motion planner to generate controlled fluid animations.Doctor of Philosoph

Collective control of modular soft robots via embodied Spiking Neural Cellular Automata

Voxel-based Soft Robots (VSRs) are a form of modular soft robots, composed of several deformable cubes, i.e., voxels. Each VSR is thus an ensemble of simple agents, namely the voxels, which must cooperate to give rise to the overall VSR behavior. Within this paradigm, collective intelligence plays a key role in enabling the emerge of coordination, as each voxel is independently controlled, exploiting only the local sensory information together with some knowledge passed from its direct neighbors (distributed or collective control). In this work, we propose a novel form of collective control, influenced by Neural Cellular Automata (NCA) and based on the bio-inspired Spiking Neural Networks: the embodied Spiking NCA (SNCA). We experiment with different variants of SNCA, and find them to be competitive with the state-of-the-art distributed controllers for the task of locomotion. In addition, our findings show significant improvement with respect to the baseline in terms of adaptability to unforeseen environmental changes, which could be a determining factor for physical practicability of VSRs.Comment: Workshop on "From Cells to Societies: Collective Learning across Scales" at the International Conference on Learning Representations (Cells2Societies@ICLR

DESIGN, MODELING, AND CONTROL OF SOFT DYNAMIC SYSTEMS

Soft physical systems, be they elastic bodies, fluids, and compliant-bodied creatures, are ubiquitous in nature. Modeling and simulation of these systems with computer algorithms enable the creation of visually appealing animations, automated fabrication paradigms, and novel user interfaces and control mechanics to assist designers and engineers to develop new soft machines. This thesis develops computational methods to address the challenges emerged during the automation of the design, modeling, and control workflow supporting various soft dynamic systems. On the design/control side, we present a sketch-based design interface to enable non-expert users to design soft multicopters. Our system is endorsed by a data-driven algorithm to generate system identification and control policies given a novel shape prototype and rotor configurations. We show that our interactive system can automate the workflow of different soft multicopters\u27 design, simulation, and control with human designers involved in the loop. On the modeling side, we study the physical behaviors of fluidic systems from a local, collective perspective. We develop a prior-embedded graph network to uncover the local constraint relations underpinning a collective dynamic system such as particle fluid. We also proposed a simulation algorithm to model vortex dynamics with locally interacting Lagrangian elements. We demonstrate the efficacy of the two systems by learning, simulating and visualizing complicated dynamics of incompressible fluid