NeuralDMD: Interpretable Neural Representation of Dynamics from Sparse and Noisy Measurements

Many challenges in scientific imaging involve solving ill-posed inverse problems, where the goal is to recover spatio-temporal fields from indirect, noisy, and highly sparse measurements - often without access to ground truth data or reliable simulators. To address this challenging scenario, we pres