Matching a model's architectural symmetries to the actual symmetries present in your data—not just the underlying physics—significantly improves performance and data efficiency.
Velocityformer is a specialized neural network that reconstructs galaxy velocities from survey data to improve cosmological measurements. By designing the model to match the asymmetric structure of real observations (where one direction—the line of sight—is special), it achieves 35% better accuracy than traditional methods and works well even with very limited training data.