arXiv:2505.09063v2 Announce Type: replace-cross Abstract: Solving time-dependent parametric partial differential equations (PDEs) with high-fidelity solvers is expensive in multi-query settings. This motivates reduced-order models in latent space. Existing methods are typically either (i) recurrent latent dynamics models, which require step-by-step integration and accumulate error over long horizons, or (ii) neural operators, which enable direct prediction but are usually trained from initial conditions and do not explicitly enforce generalization from intermediate states. We introduce the Latent Evolution Operator (LEO), a parameterized map that takes a state at an arbitrary time, together with physical parameters and a forecast horizon, and predicts the future state in a single latent-space evaluation. Under standard manifold and regularity assumptions, finite-time evolution operators admit an encoder-propagator-decoder decomposition. We approximate this structure using neural networks trained on state pairs sampled across anchor times and horizons. We evaluate the method on the 1D Burgers' equation, a 2D advection-diffusion system, and the shallow-water equations. The model produces accurate predictions across parameters and time horizons, achieves significant speedups compared to Autoencoder-LSTM baselines, and maintains accuracy when initialized from intermediate states. These results indicate that learning finite-time evolution operators in latent space provides an effective alternative to recurrent latent dynamics models and operator learning methods trained only from initial conditions.