33 citations · 60 across the 7 of their papers we have counts for
3 papers · 1 filter
Enforcing exact physics in scientific machine learning: a data-driven exterior calculus on graphs
Nathaniel Trask, Andy Huang, Xiaozhe Hu
As traditional machine learning tools are increasingly applied to science and engineering applications, physics-informed methods have emerged as effective tools for endowing infere…
Data-driven learning of robust nonlocal physics from high-fidelity synthetic data
Huaiqian You, Yue Yu, Nathaniel Trask +2
A key challenge to nonlocal models is the analytical complexity of deriving them from first principles, and frequently their use is justified a posteriori. In this work we extract…
A unified, stable and accurate meshfree framework for peridynamic correspondence modeling. Part I: core methods
Masoud Behzadinasab, Nathaniel Trask, Yuri Bazilevs
The overarching goal of this work is to develop an accurate, robust, and stable methodology for finite deformation modeling using strong-form peridynamics (PD) and the corresponden…