287 citations · 505 across the 9 of their papers we have counts for
5 papers · 1 filter
On sparse regression, Lp-regularization, and automated model discovery
Jeremy A. McCulloch, Skyler R. St. Pierre, Kevin Linka +1
Sparse regression and feature extraction are the cornerstones of knowledge discovery from massive data. Their goal is to discover interpretable and predictive models that provide s…
Discovering a reaction-diffusion model for Alzheimer's disease by combining PINNs with symbolic regression
Zhen Zhang, Zongren Zou, Ellen Kuhl +1
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 follow…
Utilising physics-guided deep learning to overcome data scarcity
Jinshuai Bai, Laith Alzubaidi, Qingxia Wang +3
Deep learning (DL) relies heavily on data, and the quality of data influences its performance significantly. However, obtaining high-quality, well-annotated datasets can be challen…
A new family of Constitutive Artificial Neural Networks towards automated model discovery
Kevin Linka, Ellen Kuhl
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 materials…
Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems
Kevin Linka, Amelie Schafer, Xuhui Meng +3
Understanding real-world dynamical phenomena remains a challenging task. Across various scientific disciplines, machine learning has advanced as the go-to technology to analyze non…