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8/6/22 16:30 - 18:30 Deep Learning in Scientific Computing III Minisymposium organized by Manuel Jesus Castro Diaz, Siddharta Mishra and David Pardo |
Room: Lounge A2 MS110C Chair: Manuel J. Castro CoChair: David Pardo |
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A machine learning minimal residual method for solving quantities of interest of parametric PDEs
Ignacio Brevis, Ignacio Muga, David Pardo, Oscar Rodríguez and Kristoffer G. van der Zee Using Graph Neural Network for gas-liquid interface reconstruction in Volume Of Fluid methods Michele-Alessandro BUCCI, Jean-Marc GRATIEN, Thibault FANEY and Tamon NAKANO Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based reduced order models Federico Fatone, Stefania Fresca and Andrea Manzoni Parameter estimation for differential problems through multi-fidelity physics-informed neural networks Francesco Regazzoni, Stefano Pagani, Alessandro Cosenza, Alessandro Lombardi and Alfio Quarteroni A collocation method based on single-layer feedforward neural network for the resolution of Elliptic PDEs Francesco Calabrò A Physics-Informed Deep Learning approach to computing solutions of hyperbolic problems Rafael Carniello, João Florindo and Eduardo Abreu A novel Machine Learning method for accurate and real-time numerical simulations of cardiac electromechanics Luca Dede‘, Francesco Regazzoni, Matteo Salvador and Alfio Quarteroni |
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