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This paper introduces a physics-aware autoencoder-based latent-space framework for reduced-order forward modeling and variational parameter estimation in parametric dynamical systems, demonstrated on computational fluid dynamics benchmarks. The method enables differentiable surrogate-based inverse modeling and shows improved calibration robustness under realistic noisy or partial observations.
This paper introduces low-cost High-Order Singular Value Decomposition (lcHOSVD), a tensor-based method for reconstructing high-dimensional environmental fields from sparse sensor measurements. Applied to urban flow and air-quality datasets, it achieves lower reconstruction errors and greater robustness to uneven sensor distributions compared to matrix-based approaches.
Physics-conforming Latent Twins is a framework for learning latent surrogate solution operators that enforce physical principles such as conservation laws and dissipative inequalities by design, using a constraint-transfer approach and structure-preserving latent dynamics.
Proposes the Mamba-Assisted Closure (MAC) framework, a Mamba-based sequence model for non-Markovian closure in reduced-order modeling of high-dimensional dynamical systems, outperforming GRU-based and Markovian methods on Burgers' equation and Lorenz '96 systems.