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 …

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 …

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

Quantifying biomechanical properties of the human vasculature could deepen our
understanding of cardiovascular diseases. Standard nonlinear regression in constitutive …

We address two major challenges in scientific machine learning (SciML): interpretability and
computational efficiency. We increase the interpretability of certain learning processes by …

Uncertainty quantification (UQ) in scientific machine learning (SciML) combines the powerful
predictive power of SciML with methods for quantifying the reliability of the learned models …

Data-driven methods have changed the way we understand and model materials. However,
while providing unmatched flexibility, these methods have limitations such as reduced …

Kolmogorov-Arnold Networks (KANs) were recently introduced as an alternative
representation model to MLP. Herein, we employ KANs to construct physics-informed …

We extend a recently proposed machine-learning-based iterative solver, ie the hybrid
iterative transferable solver (HINTS), to solve the scattering problem described by the …

Multiphysics problems that are characterized by complex interactions among fluid dynamics,
heat transfer, structural mechanics, and electromagnetics, are inherently challenging due to …