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Found 4 papers, 1 papers with code
Multi-fidelity reduced-order surrogate modeling
High-fidelity numerical simulations of partial differential equations (PDEs) given a restricted computational budget can significantly limit the number of parameter configurations considered and/or time window evaluated for modeling a given system.
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 and entail expensive computational costs, which might become prohibitive when approximating steady-state solutions of PDEs for multiple combinations of control parameters and initial conditions.
Multi-fidelity surrogate modeling using long short-term memory networks
Especially for parametrized, time dependent problems in engineering computations, it is often the case that acceptable computational budgets limit the availability of high-fidelity, accurate simulation data.
Multi-fidelity regression using artificial neural networks: efficient approximation of parameter-dependent output quantities
In this work, we present the use of artificial neural networks applied to multi-fidelity regression problems.
