Adversarial Disentanglement by Backpropagation with Physics-Informed Variational Autoencoder
This addresses the problem of poor generalization in data-driven models for engineering structures by integrating physics-based interpretability, but it appears incremental as it builds on existing variational autoencoder and adversarial training methods.
The paper tackled the challenge of inference and prediction in physical systems with confounding influences by proposing a physics-informed variational autoencoder that partitions the latent space into physics-based and data-driven variables, using adversarial training to maintain interpretability; it demonstrated feasibility on synthetic engineering case studies, though no concrete performance numbers were provided.
Inference and prediction under partial knowledge of a physical system is challenging, particularly when multiple confounding sources influence the measured response. Explicitly accounting for these influences in physics-based models is often infeasible due to epistemic uncertainty, cost, or time constraints, resulting in models that fail to accurately describe the behavior of the system. On the other hand, data-driven machine learning models such as variational autoencoders are not guaranteed to identify a parsimonious representation. As a result, they can suffer from poor generalization performance and reconstruction accuracy in the regime of limited and noisy data. We propose a physics-informed variational autoencoder architecture that combines the interpretability of physics-based models with the flexibility of data-driven models. To promote disentanglement of the known physics and confounding influences, the latent space is partitioned into physically meaningful variables that parametrize a physics-based model, and data-driven variables that capture variability in the domain and class of the physical system. The encoder is coupled with a decoder that integrates physics-based and data-driven components, and constrained by an adversarial training objective that prevents the data-driven components from overriding the known physics, ensuring that the physics-grounded latent variables remain interpretable. We demonstrate that the model is able to disentangle features of the input signal and separate the known physics from confounding influences using supervision in the form of class and domain observables. The model is evaluated on a series of synthetic case studies relevant to engineering structures, demonstrating the feasibility of the proposed approach.