The rapid growth in miniaturization of low-power embedded devices and advancement in
the optimization of machine learning (ML) algorithms have opened up a new prospect of the …

The semantic image segmentation task consists of classifying each pixel of an image into an
instance, where each instance corresponds to a class. This task is a part of the concept of …

Despite the fact that deep neural networks are powerful models and achieve appealing
results on many tasks, they are too large to be deployed on edge devices like smartphones …

Abstract Knowledge Distillation (KD) consists of transferring “knowledge” from one machine
learning model (the teacher) to another (the student). Commonly, the teacher is a high …

This review article highlights state-of-the-art data-driven techniques to discover, encode,
surrogate, or emulate constitutive laws that describe the path-independent and path …

We introduce a deep learning framework designed to train smoothed elastoplasticity models
with interpretable components, such as the stored elastic energy function, yield surface, and …

Physics-Informed Neural Networks (PINN) are algorithms from deep learning leveraging
physical laws by including partial differential equations together with a respective set of …

Efficiently embedding and/or integrating mechanistic information with data-driven models is
essential if it is desired to simultaneously take advantage of both engineering principles and …

We present a machine learning approach that integrates geometric deep learning and
Sobolev training to generate a family of finite strain anisotropic hyperelastic models that …

Large language models (LLMs) can spend extra compute during inference to generate
intermediate thoughts, which helps to produce better final responses. Since Chain-of …