Tag
Introduces in-span learning, a method to adapt reduced-order models by streaming the model's own predictions through an incremental singular-value decomposition, reweighting and realigning the basis without changing the subspace. The approach is demonstrated on several dynamical systems and proposed as a computational-science analogue of in-context learning.
This paper introduces a fully GPU-based workflow that accelerates data generation and training of neural emulators for hypersonic flows, using a differentiable solver (JAX-Fluids) and residual-based refinement to improve physical consistency and reliability beyond training distribution.
This paper proposes structure-preserving neural surrogates for partial differential equations that integrate Gaussian process regression to provide tractable uncertainty quantification, enabling real-time simulation with closed-form error estimates.
This paper proposes an uncertainty-aware multi-fidelity framework based on conditional normalizing flows to improve the predictive accuracy of reduced-order models (ROMs) for complex multiscale systems. The method learns a probabilistic mapping from low-fidelity to high-fidelity coefficients and is demonstrated on a vortex merging problem, showing improved accuracy with uncertainty quantification.