Characterizing and Mitigating Distributional Bias in Physics-Informed Generative Models
Physics-informed generative models augment a data-fitting objective with residual penalties that encourage physical constraints to hold in generated samples.
ProofPaper ↗
Key points
- However, improving constraint satisfaction need not improve fidelity to the data distribution.
- We study this conflict in residual-regularized diffusion models and identify two possible consequences.
- Motivated by this analysis, we propose a data-anchored penalty that weights prediction errors by the residual Jacobian at the clean training sample and normalizes the resulting magnitude.
- Experiments on a controlled synthetic system, three PDE systems, and molecular transition-state generation with flow matching demonstrate the benefits of anchoring.
Sources (1)
- [1]Characterizing and Mitigating Distributional Bias in Physics-Informed Generative ModelsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 11:02 AM
Physics-informed generative models augment a data-fitting objective with residual penalties that encourage physical constraints to hold in generated samples.
However, improving constraint satisfaction need not improve fidelity to the data distribution.
Extractive summary: sentences quoted from the sources.
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