Differentiable Autoencoding Neural Operator for Interpretable and Integrable Latent Space Modeling
This work addresses the problem of interpretability and integrability in latent space modeling for scientific machine learning, particularly in fluid dynamics applications, though it appears incremental as it builds upon existing neural operator and autoencoder techniques.
The authors tackled the challenge of achieving interpretable latent spaces in scientific machine learning for high-dimensional spatiotemporal flow data by proposing DIANO, a differentiable autoencoding neural operator framework that constructs physically interpretable latent spaces and integrates PDE solvers, demonstrating its capability on benchmark problems like flow past a 2D cylinder and 3D patient-specific coronary artery.
Scientific machine learning has enabled the extraction of physical insights from high-dimensional spatiotemporal flow data using linear and nonlinear dimensionality reduction techniques. Despite these advances, achieving interpretability within the latent space remains a challenge. To address this, we propose the DIfferentiable Autoencoding Neural Operator (DIANO), a deterministic autoencoding neural operator framework that constructs physically interpretable latent spaces for both dimensional and geometric reduction, with the provision to enforce differential governing equations directly within the latent space. Built upon neural operators, DIANO compresses high-dimensional input functions into a low-dimensional latent space via spatial coarsening through an encoding neural operator and subsequently reconstructs the original inputs using a decoding neural operator through spatial refinement. We assess DIANO's latent space interpretability and performance in dimensionality reduction against baseline models, including the Convolutional Neural Operator and standard autoencoders. Furthermore, a fully differentiable partial differential equation (PDE) solver is developed and integrated within the latent space, enabling the temporal advancement of both high- and low-fidelity PDEs, thereby embedding physical priors into the latent dynamics. We further investigate various PDE formulations, including the 2D unsteady advection-diffusion and the 3D Pressure-Poisson equation, to examine their influence on shaping the latent flow representations. Benchmark problems considered include flow past a 2D cylinder, flow through a 2D symmetric stenosed artery, and a 3D patient-specific coronary artery. These case studies demonstrate DIANO's capability to solve PDEs within a latent space that facilitates both dimensional and geometrical reduction while allowing latent interpretability.