According to DeepLearning.AI, researchers introduced Walrus, a transformer model that predicts fluid behavior across liquids, gases, and plasmas with higher accuracy and more stable long‑term rollouts than prior baselines, aided by a jitter technique that mitigates error accumulation during iterative simulations. As reported by DeepLearning.AI’s The Batch, Walrus generalizes across multiple physical domains, indicating opportunities to replace or accelerate parts of computational fluid dynamics pipelines, reduce GPU hours for engineering design loops, and enable faster what‑if analyses in climate, aerospace, and energy simulations. According to DeepLearning.AI, the jitter training strategy injects controlled perturbations into autoregressive steps, improving robustness to compounding errors over long horizons, which is critical for production forecasting and digital twin stability.