Deep learning (DL) is revolutionizing evidence-based decision-making techniques that can
be applied across various sectors. Specifically, it possesses the ability to utilize two or more …

Artificial intelligence, especially machine learning (ML) and deep learning (DL) algorithms,
is becoming an important tool in the fields of materials and mechanical engineering …

Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …

Characterizing internal structures and defects in materials is a challenging task, often
requiring solutions to inverse problems with unknown topology, geometry, material …

Failure trajectories, probable failure zones, and damage indices are some of the key
quantities of relevance in brittle fracture mechanics. High-fidelity numerical solvers that …

Despite its rapid development, Physics-Informed Neural Network (PINN)-based
computational solid mechanics is still in its infancy. In PINN, the loss function plays a critical …

With massive advancements in sensor technologies and Internet-of-things (IoT), we now
have access to terabytes of historical data; however, there is a lack of clarity on how to best …

Standard neural networks can approximate general nonlinear operators, represented either
explicitly by a combination of mathematical operators, eg in an advection–diffusion reaction …

Recently, a class of machine learning methods called physics-informed neural networks
(PINNs) has been proposed and gained prevalence in solving various scientific computing …

In this paper, we introduce a new approach based on distance fields to exactly impose
boundary conditions in physics-informed deep neural networks. The challenges in satisfying …