MF-LNO, a multi-fidelity Laplace neural operator with reSGLD uncertainty sampling, beats single-fidelity LNO baselines by 40-80% on four parametric ODE/PDE test problems.
Physics-Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations
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Muti-Fidelity Prediction and Uncertainty Quantification with Laplace Neural Operators for Parametric Partial Differential Equations
MF-LNO, a multi-fidelity Laplace neural operator with reSGLD uncertainty sampling, beats single-fidelity LNO baselines by 40-80% on four parametric ODE/PDE test problems.