Deep Learning

The rise of exascale supercomputing has motivated an increase in high-fidelity computational fluid dynamics (CFD) simulations. The detail in these simulations, often involving shape-dependent, time-variant flow domains and low-speed, complex, turbulent flows, is essential for fueling innovations in fields like wind, civil, automotive, or aerospace engineering. However, the massive amount of data these simulations produce can overwhelm storage systems and negatively affect conventional data management and postprocessing workflows, including iterative procedures such as design space exploration, optimization, and uncertainty quantification. This study proposes a novel sampling method harnessing the signed distance function (SDF) concept, SDF-biased flow importance sampling (BiFIS) and implicit compression based on implicit neural network representations for transforming large-size, shape-dependent flow fields into reduced-size shape-agnostic images.

This work explores the integration of generative AI models for automatically generating synthetic image-labeled data. Our approach leverages controllable Diffusion Models to generate synthetic variations of semantically labeled images. Synthetic datasets for semantic segmentation struggle to represent real-world subtleties, such as different weather conditions or fine details, typically relying on costly simulations and rendering. However, Diffusion Models can generate diverse images using input text prompts and guidance images, like semantic masks. Our work introduces and tests a novel methodology for generating labeled synthetic images, with an initial focus on semantic segmentation, a demanding computer vision task.

Rendering 3D virtual scenarios has become a popular alternative for generating per-pixel-labeled image datasets, especially in fields like autonomous driving. The approach is valuable for training neural perception models, such as semantic segmentation models, particularly when data might be scarce, expensive, or difficult to collect. However, fundamental questions persist within the research community regarding the generation and processing of these synthetic images, particularly a better understanding of the key factors influencing the performance of deep learning models trained with such synthetic images.

Aero-structural shape design and optimization of bridge decks rely on accurately estimating their self-excited aeroelastic forces within the design domain. The inherent nonlinear features of bluff body aerodynamics and the high cost of wind tunnel tests and computational fluid dynamics (CFD) simulations make their emulation as a function of deck shape and reduced velocity challenging. We propose a time domain emulation strategy harnessing temporal fusion transformers (TFTs) to predict the self-excited forces time series before their integration into FDs.

This Python library specializes in the generation of synthetic tabular data. It has a diverse range of statistical, Machine Learning (ML) and Deep Learning (DL) methods to accurately capture patterns in real datasets and synthetically replicate them.

Real-time play recognition and classification algorithms are crucial for automating video production and live broadcasts of sporting events. However, current methods relying on human pose estimation and deep neural networks introduce high latency on commodity hardware, limiting their usability in low-cost real-time applications. We present PlayNet, a novel approach to real-time handball play classification.