no code implementations • 2 Feb 2022 • Søren Taverniers, Svyatoslav Korneev, Kyle M. Pietrzyk, Morad Behandish
Predicting part quality for additive manufacturing (AM) processes requires high-fidelity numerical simulation of partial differential equations (PDEs) governing process multiphysics on a scale of minimum manufacturable features.
no code implementations • 7 Sep 2020 • Søren Taverniers, Eric J. Hall, Markos A. Katsoulakis, Daniel M. Tartakovsky
Timely completion of design cycles for complex systems ranging from consumer electronics to hypersonic vehicles relies on rapid simulation-based prototyping.
no code implementations • 26 Jun 2020 • Eric J. Hall, Søren Taverniers, Markos A. Katsoulakis, Daniel M. Tartakovsky
We introduce the concept of a Graph-Informed Neural Network (GINN), a hybrid approach combining deep learning with probabilistic graphical models (PGMs) that acts as a surrogate for physics-based representations of multiscale and multiphysics systems.