Aug 2026· Machine Learning for Computational Science and Engineering· Vol 2· 0 citations· 53 references
TL;DR
A Fourier-enhanced operator autoencoder for decoder-free reconstruction and latent learning of dynamical systems and achieves accuracy comparable to or better than classical AE-based reduced-order models while providing a more efficient latent-to-field reconstruction path.
Model-based active flow control requires predictive models that are accurate, stable, and fast enough for real-time optimisation. In controlled wake flows, this is often achieved through Reduced-Order Models (ROMs) that first compress high-dimensional velocity snapshots into a latent space and then learn a time- stepping predictor for the dynamics in the latent space. Here, we study how the choice of the spatial encoder affects the predictability of the resulting latent coordinates for wake flows under control inputs. Using two actuated 2D wake configurations, a simplified truck wake and the fluidic pinball, we compare Proper Orthogonal Decomposition (POD) against nonlinear Convolutional Autoencoders (CAEs) and two types of variational autoencoders for compression, and evaluate several temporal predictors based on Long Short-Term Memory networks. CAEs achieve higher compression efficiency and sharper short-term reconstructions, but they produce latent dynamics that are more irregular and with broadband spectral content. As a consequence, long-horizon forecasts degrade faster and show a higher probability of catastrophic divergence than POD-based models. POD yields smoother latent trajectories that are easier to learn and extrapolate, leading to more reliable predictions beyond the short- term regime. These results reveal a clear trade-off between compactness and forecast accuracy, and suggest that the stability of the latent dynamics prediction can outweigh maximal compression. This is particularly relevant for control strategies rooted in forecasts of the dynamics, such as model predictive control and reinforcement learning. The findings provide practical guidance for designing actuation-aware, hardware-feasible predictive ROMs for real-time flow control.
A. Solera-Rico, Patricia Garc'ia-Caspuenas, Carlos Sanmiguel Vila et al.· 1 citation
It is argued that the outstanding obstacle to deploying world models in systems that cannot fail -- power, thermal, process control -- is not predictive fidelity but verifiability, and a research agenda for physics-grounded, verifiable world models is outlined that unifies the two lineages.
A lightweight and projection-free autoencoder that directly outputs compact, Contrastive Language–Image Pre-training (CLIP)-scale latent vectors trained toward the CLIP embedding space, achieving superior signal reconstruction fidelity across time and frequency domains using significantly fewer parameters than traditional masked autoencoder baselines.
Jeyoung Lee, Jaekwan Ahn, Jaeseung Sim et al.· Italian National Conference...· 0 citations
This work introduces a novel class of symmetric Convolutional AutoEncoders (CAEs) designed to embody the primary properties of manifold parametrization mappings and demonstrates significantly improved predictive capabilities when integrated into a ROM framework.
Gaspare LI Causi, Niccolò Tonicello, Luca Magri et al.· 0 citations
A physics-aware neural-network-based latent-space framework for reduced-order forward modeling and variational parameter estimation and the robustness of the proposed framework is demonstrated, which generally reduces calibration error and variability compared with the standard surrogate models.
Qiyao Zhou, Xujia Zhu, Pierre Joli et al.· 0 citations
The discovered that a complementary and previously unexploited in-span adaptation channel exists within the current reduced subspace, and obtains a trajectory-informed spectral preconditioner, in which the subspace is unchanged but the basis is reweighted and realigned toward the modes visited by the dynamics.
Amirpasha Hedayat, Laura Balzano, Karthik Duraisamy· 0 citations