Physics-Aware Constraints for Inverse Shape Design

Neural surrogates now carry computer aided engineering (CAE) into inverse shape design, where optimization descends the gradients of a frozen model. However, the optimizer and the judge are the same network: the search freely leaves the region where the surrogate has evidence, claiming gains that no data can verify. Today this trust gap is bridged by hand, with engineers specifying for every task where a shape may deform and how far it may drift. We present Physics-Aware Constraints (PAC), the first supervision layer to read both constraints from the frozen GeoPT, pretrained on geometry lifted with synthetic dynamics. The core insight is that a model pretrained to perceive geometry through synthetic physical response should supervise the search rather than drive it. The model’s weights bound how far optimization may drift off the family response manifold, and its distance field bounds where deformation is admissible, both calibrated on the family without labels. Across four CAE families, PAC consistently keeps optimization inside the trust band at little cost to legitimate gains, delivering on ship hulls a 64% wave drag reduction verified by an analytic oracle. The results separate the two sources of the constraint: its scale selectivity comes from the distance field representation and its detection sensitivity from physics lifted pretraining. PAC performs the judgment of a delivered design that autonomous design agents in industry still leave to an engineer, moving their full automation a step closer.