Generality and robustness of learned simulators under severe changes

Determine the extent to which learned simulators for continuum mechanics can generalize to severe changes in domain geometry, boundary conditions, and constitutive laws while maintaining robustness (i.e., avoiding hallucinations) and accuracy during inference.

Background

The paper investigates whether foundation model principles can be applied to learned simulators in continuum mechanics (solid and fluid). While learned simulators provide fast inference and have shown success in specific tasks, their reliability depends on training data quality and they risk producing physically meaningless outputs due to complex loss landscapes.

A central bottleneck highlighted is the lack of generality: models trained on particular setups often underperform when the domain geometry, boundary conditions, or material constitutive laws change. The authors frame the question of generalization—especially zero-shot or minimal adaptation scenarios—as a key unresolved issue, motivating their worst-case evaluation strategy and adaptation techniques (e.g., transfer learning and thermodynamic inductive biases).