A space-time reduced simulation framework combining proper generalized decomposition with the partition of unity method accelerates the computation of long-term ...
Researchers developed a projection-based reduced-order model integrating isogeometric analysis and energy-conserving hyper-reduction to rapidly simulate parameterized liquid sloshing in aerospace ...
A deep learning framework using conditional convolutional autoencoders directly on structured computational grids cut aerodynamic flow simulation time to a ...
Researchers optimized projection bases through dissipative operator refitting, significantly reducing simulation residuals and trajectory errors in nonlinear port-Hamiltonian systems.
A comparative analysis reveals that non-intrusive convolutional autoencoders capture nonlinear mechanical strains in metal additive manufacturing more accurately than traditional ...
Researchers have developed a reduced-order model that captures how peak tapping forces, residual gaps, and humidity govern electrical output in ...
Aerospace researchers have demonstrated a time-varying-delay active disturbance rejection controller that stabilizes airfoil flutter across transonic speeds despite fluctuating control-loop ...
Researchers built regressor chain surrogate models that predict pressure and stress fields across varied wing planforms with modest prediction errors ...
Researchers applied proper generalized decomposition to transient thermoelastic analysis and cut computation time to roughly one-fourth of their in-house finite ...
A new two-stage reduced order model combines hyper-reduction and Gaussian process regression to cut both training and prediction costs for ...
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