For more than 100 years, chemical, physical, and material scientists have proposed
competing constitutive models to best characterize the behavior of natural and man-made …

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

Physics informed neural networks (PINNs) are capable of finding the solution for a given
boundary value problem. Here, the training of the network is equivalent to the minimization …

The brain is our softest and most vulnerable organ, and understanding its physics is a
challenging but significant task. Throughout the past decade, numerous competing models …

Predictive maintenance for power electronic converters has emerged as a critical area of
research and development. With the rapid advancements in deep-learning techniques, new …

Physics-informed neural networks (PINNs) represent an emerging computational paradigm
that incorporates observed data patterns and the fundamental physical laws of a given …

Misfolded tau proteins play a critical role in the progression and pathology of Alzheimer's
disease. Recent studies suggest that the spatio-temporal pattern of misfolded tau follows a …

Uncertainty quantification (UQ) in scientific machine learning (SciML) becomes increasingly
critical as neural networks (NNs) are being widely adopted in addressing complex problems …

Data-driven discovery of governing equations in computational science has emerged as a
new paradigm for obtaining accurate physical models and as a possible alternative to …

The stiffness of soft biological tissues not only depends on the applied deformation, but also
on the deformation rate. To model this type of behavior, traditional approaches select a …