Differentiation Strategies for Acoustic Inverse Problems: Admittance Estimation and Shape Optimization

We demonstrate a practical differentiable programming approach for acoustic inverse problems through two applications: admittance estimation and shape optimization for resonance damping. First, we show that JAX-FEM's automatic differentiation (AD) enables direct gradient-based estimation of complex boundary admittance from sparse pressure measurements, achieving 3-digit precision without requiring manual derivation of adjoint equations. Second, we apply randomized finite differences to acoustic shape optimization, combining JAX-FEM for forward simulation with PyTorch3D for mesh manipulation through AD. By separating physics-driven boundary optimization from geometry-driven interior mesh adaptation, we achieve 48.1% energy reduction at target frequencies with 30-fold fewer FEM solutions compared to standard finite difference on the full mesh. This work showcases how modern differentiable software stacks enable rapid prototyping of optimization workflows for physics-based inverse problems, with automatic differentiation for parameter estimation and a combination of finite differences and AD for geometric design.

Surrogate-Based Differentiable Pipeline for Shape Optimization

Gradient-based optimization of engineering designs is limited by non-differentiable components in the typical computer-aided engineering (CAE) workflow, which calculates performance metrics from design parameters. While gradient-based methods could provide noticeable speed-ups in high-dimensional design spaces, codes for meshing, physical simulations, and other common components are not differentiable even if the math or physics underneath them is. We propose replacing non-differentiable pipeline components with surrogate models which are inherently differentiable. Using a toy example of aerodynamic shape optimization, we demonstrate an end-to-end differentiable pipeline where a 3D U-Net full-field surrogate replaces both meshing and simulation steps by training it on the mapping between the signed distance field (SDF) of the shape and the fields of interest. This approach enables gradient-based shape optimization without the need for differentiable solvers, which can be useful in situations where adjoint methods are unavailable and/or hard to implement.

Sparse multimodal fusion with modal channel attention

The ability of masked multimodal transformer architectures to learn a robust embedding space when modality samples are sparsely aligned is studied by measuring the quality of generated embedding spaces as a function of modal sparsity. An extension to the masked multimodal transformer model is proposed which incorporates modal-incomplete channels in the multihead attention mechanism called modal channel attention (MCA). Two datasets with 4 modalities are used, CMU-MOSEI for multimodal sentiment recognition and TCGA for multiomics. Models are shown to learn uniform and aligned embedding spaces with only two out of four modalities in most samples. It was found that, even with no modal sparsity, the proposed MCA mechanism improves the quality of generated embedding spaces, recall metrics, and subsequent performance on downstream tasks.