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Reduced order modeling of parametrized systems through autoencoders and SINDy approach: continuation of periodic solutions
Highly accurate simulations of complex phenomena governed by partial differential equations (PDEs) typically require intrusive methods …
Paolo Conti, Giorgio Gobat, Stefania Fresca, Andrea Manzoni, Attilio Frangi
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Reduced order modeling of parametrized systems through autoencoders and SINDy approach: continuation of periodic solutions
Multi-fidelity surrogate modeling using long short-term memory networks
When evaluating quantities of interest that depend on the solutions to differential equations, we inevitably face the trade-off between …
Paolo Conti, Mengwu Guo, Andrea Manzoni, Jan S. Hesthaven
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Multi-fidelity surrogate modeling using long short-term memory networks
Multi-fidelity regression using artificial neural networks: Efficient approximation of parameter-dependent output quantities
Highly accurate numerical or physical experiments are often very time-consuming or expensive to obtain. When time or budget …
Mengwu Guo, Andrea Manzoni, Maurice Amendt, Paolo Conti, Jan S. Hesthaven
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Multi-fidelity regression using artificial neural networks: Efficient approximation of parameter-dependent output quantities