To allow for scalable inference, we implement our model as a variational autoencoder and perform amortized variational inference.

High-resolution imaging from array data in unknown inhomogeneous ambient media requires estimating both the medium properties and the object characteristics. For diverse measurements collected from different sources in different, changing media, we introduce in this paper an algorithm that recovers the ambient media properties needed for high-resolution imaging as well as the source locations and strengths that constitute the imaging target. This algorithm extends and improves upon our previous work on imaging through random media using array data. Previously, we addressed imaging through a single unknown random medium, either weakly scattering [ 1 ] or strongly scattering [ 2 ].

Moreover, we qualitatively demonstrate our model's prediction quality by Please use headphones to hear the spatial audio correctly. As we can see, the prediction error tends to be small when the source is relatively close to the receiver, or there are no major obstacles along the path connecting them. We show two scenes and two examples per scene. For our experiment with ambient environment sounds (Sec. We will publish the link to our datasets on our project page. Here, we provide our architecture and additional training details for reproducibility.

Abstract-- This paper proposes an uncertainty-aware marine pollution source tracking framework for unmanned surface vehicles (USVs). By integrating high-fidelity marine pollution dispersion simulation with informative path planning techniques, we demonstrate effective identification of pollution sources in marine environments. The proposed approach is implemented based on Robot Operating System (ROS), processing real-time sensor data to update probabilistic source location estimates. Experiments conducted in simulated environments with varying source locations, wave conditions, and starting positions demonstrate the framework's ability to localise pollution sources with high accuracy. Results show that the proposed approach achieves reliable source localisation efficiently and outperforms the existing baseline. This work contributes to the development of full autonomous environmental monitoring capabilities essential for rapid response to marine pollution incidents. Pollution discharged into the marine environment causes severe consequences to ecosystems [1], [2] and human health [3].

We present a machine learning framework for adaptive source localization in which agents use a structure-preserving digital twin of a coupled hydrodynamic-transport system for real-time trajectory planning and data assimilation. The twin is constructed with conditional neural Whitney forms (CNWF), coupling the numerical guarantees of finite element exterior calculus (FEEC) with transformer-based operator learning. The resulting model preserves discrete conservation, and adapts in real time to streaming sensor data. It employs a conditional attention mechanism to identify: a reduced Whitney-form basis; reduced integral balance equations; and a source field, each compatible with given sensor measurements. The induced reduced-order environmental model retains the stability and consistency of standard finite-element simulation, yielding a physically realizable, regular mapping from sensor data to the source field. We propose a staggered scheme that alternates between evaluating the digital twin and applying Lloyd's algorithm to guide sensor placement, with analysis providing conditions for monotone improvement of a coverage functional. Using the predicted source field as an importance function within an optimal-recovery scheme, we demonstrate recovery of point sources under continuity assumptions, highlighting the role of regularity as a sufficient condition for localization. Experimental comparisons with physics-agnostic transformer architectures show improved accuracy in complex geometries when physical constraints are enforced, indicating that structure preservation provides an effective inductive bias for source identification.

To allow for scalable inference, we implement our model as a variational autoencoder and perform amortized variational inference.

M-Step only reconstructions with known sources . . . . . . . . . . . . . . The velocity reconstruction MSE is included in the top right of each reconstruction. V elocity reconstructions corresponding to the sources reconstructed in Figure 1. The velocity reconstruction MSE is included in the top right of each reconstruction, where the true Earth velocity is shown in Figure 1(a) of the main paper. Note that as the number of sources increase, the MSE tends to improve.