AIF-TVR: Active Inference for Time-Varying 3D Flow Field Reconstruction with Sparse High-Resolution Observations

Reconstructing time-varying 3D flow fields from sparse high-resolution observations requires selecting informative frames and incorporating their evidence into reconstruction. We propose AIF-TVR, an active-inference-inspired framework that couples sequential observation acquisition with conditional diffusion reconstruction. A degradation-aware temporal belief model propagates acquired evidence across timesteps. An observation-predictive planner evaluates hypothetical belief updates to select frames using an expected-free-energy-inspired objective. The acquired observations then supervise adaptation of the diffusion reconstructor to recover the complete sequence. Experiments on three flow datasets demonstrate lower MSE and MAE than the evaluated baselines, with PSNR gains of 1.99–4.61 dB over the strongest reported baseline on each dataset.