Physics-Informed Deep Orthogonal Decomposition with Calibrated Uncertainty Quantification for Reduced-Order Modelling of Parametrized Partial Differential Equations
Solving parametrized partial differential equations thousands of times is the bottleneck of design, control, inversion, and digital-twin workflows. Reduced-order models answer it by replacing the expensive discretization with a fast surrogate. Adaptive-basis surrogates, such as the deep orthogonal decomposition, break...