49 research outputs found
A Brief Note on Building Augmented Reality Models for Scientific Visualization
Augmented reality (AR) has revolutionized the video game industry by
providing interactive, three-dimensional visualization. Interestingly, AR
technology has only been sparsely used in scientific visualization. This is, at
least in part, due to the significant technical challenges previously
associated with creating and accessing such models. To ease access to AR for
the scientific community, we introduce a novel visualization pipeline with
which they can create and render AR models. We demonstrate our pipeline by
means of finite element results, but note that our pipeline is generally
applicable to data that may be represented through meshed surfaces.
Specifically, we use two open-source software packages, ParaView and Blender.
The models are then rendered through the platform, which we
access through Android and iOS smartphones. To demonstrate our pipeline, we
build AR models from static and time-series results of finite element
simulations discretized with continuum, shell, and beam elements. Moreover, we
openly provide python scripts to automate this process. Thus, others may use
our framework to create and render AR models for their own research and
teaching activities
Generative Hyperelasticity with Physics-Informed Probabilistic Diffusion Fields
Many natural materials exhibit highly complex, nonlinear, anisotropic, and
heterogeneous mechanical properties. Recently, it has been demonstrated that
data-driven strain energy functions possess the flexibility to capture the
behavior of these complex materials with high accuracy while satisfying
physics-based constraints. However, most of these approaches disregard the
uncertainty in the estimates and the spatial heterogeneity of these materials.
In this work, we leverage recent advances in generative models to address these
issues. We use as building block neural ordinary equations (NODE) that -- by
construction -- create polyconvex strain energy functions, a key property of
realistic hyperelastic material models. We combine this approach with
probabilistic diffusion models to generate new samples of strain energy
functions. This technique allows us to sample a vector of Gaussian white noise
and translate it to NODE parameters thereby representing plausible strain
energy functions. We extend our approach to spatially correlated diffusion
resulting in heterogeneous material properties for arbitrary geometries. We
extensively test our method with synthetic and experimental data on biological
tissues and run finite element simulations with various degrees of spatial
heterogeneity. We believe this approach is a major step forward including
uncertainty in predictive, data-driven models of hyperelasticityComment: 22 pages, 11 figure
The Confidence Database
Understanding how people rate their confidence is critical for the characterization of a wide range of perceptual, memory, motor and cognitive processes. To enable the continued exploration of these processes, we created a large database of confidence studies spanning a broad set of paradigms, participant populations and fields of study. The data from each study are structured in a common, easy-to-use format that can be easily imported and analysed using multiple software packages. Each dataset is accompanied by an explanation regarding the nature of the collected data. At the time of publication, the Confidence Database (which is available at https://osf.io/s46pr/) contained 145 datasets with data from more than 8,700 participants and almost 4 million trials. The database will remain open for new submissions indefinitely and is expected to continue to grow. Here we show the usefulness of this large collection of datasets in four different analyses that provide precise estimations of several foundational confidence-related effects
Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples
Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts
An augmented iterative method for identifying a stress-free reference configuration in image-based biomechanical modeling
International audienceContinuing advances in computational power and methods have enabled image-based biomechanical modeling to become a crucial tool in basic science, diagnostic and therapeutic medicine, and medical device design. One of the many challenges of this approach, however, is the identification of a stress-free reference configuration based on in vivo images of loaded and often prestressed or residually stressed soft tissues and organs. Fortunately, iterative methods have been proposed to solve this inverse problem, among them Sellier’s method. This method is particularly appealing for it is easy toimplement, convergences reasonably fast, and can be coupled to nearly any finite element package. However, by means of several practical examples, we demonstrate that in its original formulation Sellier’s method is not optimally fast and may not converge for problems with large deformations. Fortunately, we can also show that a simple, inexpensive augmentation of Sellier’s method based on Aitken’s delta-squared process can not only ensure convergence but also significantly accelerate the method