787 research outputs found
MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under non-parameterized geometrical variability
When learning simulations for modeling physical phenomena in industrial
designs, geometrical variabilities are of prime interest. While classical
regression techniques prove effective for parameterized geometries, practical
scenarios often involve the absence of shape parametrization during the
inference stage, leaving us with only mesh discretizations as available data.
Learning simulations from such mesh-based representations poses significant
challenges, with recent advances relying heavily on deep graph neural networks
to overcome the limitations of conventional machine learning approaches.
Despite their promising results, graph neural networks exhibit certain
drawbacks, including their dependency on extensive datasets and limitations in
providing built-in predictive uncertainties or handling large meshes. In this
work, we propose a machine learning method that do not rely on graph neural
networks. Complex geometrical shapes and variations with fixed topology are
dealt with using well-known mesh morphing onto a common support, combined with
classical dimensionality reduction techniques and Gaussian processes. The
proposed methodology can easily deal with large meshes without the need for
explicit shape parameterization and provides crucial predictive uncertainties,
which are essential for informed decision-making. In the considered numerical
experiments, the proposed method is competitive with respect to existing graph
neural networks, regarding training efficiency and accuracy of the predictions
Synthesizing Normalized Faces from Facial Identity Features
We present a method for synthesizing a frontal, neutral-expression image of a
person's face given an input face photograph. This is achieved by learning to
generate facial landmarks and textures from features extracted from a
facial-recognition network. Unlike previous approaches, our encoding feature
vector is largely invariant to lighting, pose, and facial expression.
Exploiting this invariance, we train our decoder network using only frontal,
neutral-expression photographs. Since these photographs are well aligned, we
can decompose them into a sparse set of landmark points and aligned texture
maps. The decoder then predicts landmarks and textures independently and
combines them using a differentiable image warping operation. The resulting
images can be used for a number of applications, such as analyzing facial
attributes, exposure and white balance adjustment, or creating a 3-D avatar
Hull Shape Design Optimization with Parameter Space and Model Reductions, and Self-Learning Mesh Morphing
In the field of parametric partial differential equations, shape optimization represents a challenging problem due to the required computational resources. In this contribution, a data-driven framework involving multiple reduction techniques is proposed to reduce such computational burden. Proper orthogonal decomposition (POD) and active subspace genetic algorithm (ASGA) are applied for a dimensional reduction of the original (high fidelity) model and for an efficient genetic optimization based on active subspace property. The parameterization of the shape is applied directly to the computational mesh, propagating the generic deformation map applied to the surface (of the object to optimize) to the mesh nodes using a radial basis function (RBF) interpolation. Thus, topology and quality of the original mesh are preserved, enabling application of POD-based reduced order modeling techniques, and avoiding the necessity of additional meshing steps. Model order reduction is performed coupling POD and Gaussian process regression (GPR) in a data-driven fashion. The framework is validated on a benchmark ship
Over speed detection using Artificial Intelligence
Over speeding is one of the most common traffic violations. Around 41 million people are issued speeding tickets each year in USA i.e one every second. Existing approaches to detect over- speeding are not scalable and require manual efforts. In this project, by the use of computer vision and artificial intelligence, I have tried to detect over speeding and report the violation to the law enforcement officer. It was observed that when predictions are done using YoloV3, we get the best results
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