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IS13a - Physics informed machine learning for scientific applications
Corresponding Organizer: Dr. Massimiliano Lupo Pasini (Oak Ridge National Laboratory)
Chaired by:
Dr. Massimiliano Lupo Pasini (Oak Ridge National Laboratory , United States)
Dr. Massimiliano Lupo Pasini (Oak Ridge National Laboratory , United States)
Scheduled presentations:
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Keynote
Graph Neural Networks Motivated By PDE’s
E. Haber* -
Physics-Informed Machine Learning for Proper Orthogonal Decomposition (POD) based Reduced-Order Modelling (ROM) of Parametric, Partial Differential Equations (PDEs)
H. Dave*, L. Cotteleer, A. Parente -
Physics-Informed Neural Networks With Hard Constraints for Solid and Contact Mechanics
T. Sahin*, M. von Danwitz, A. Popp -
Physics-Informed Neural Networks for Granular Flows Modelling
Y. Cheny*, M. Delcey, S. Kiesgen de Richter, J. Keck -
Physics Informed Neural Networks for Gravity Currents Reconstruction from Limited Data
M. Delcey*, Y. Cheny, S. Kiesgen De Richter