Abstract With the growing popularity of artificial intelligence (AI) used for scientific applications, the ability of attribute a result to a reasoning process from the network is in high demand for robust scientific generalizations to hold. In this work we aim to motivate the need for and demonstrate the use of post-hoc explainability methods when applied to AI methods used in scientific applications. To this end, we introduce explainability add-ons to the existing graph neural network (GNN) for neutrino tagging, NuGraph2. The explanations take the form of a suite of techniques examining the output of the network (node classifications) and the edge connections between them, and probing of the latent space using novel general-purpose tools applied to this network. We show how none of these methods are singularly sufficient to show network ‘understanding’, but together can give insights into the processes used in classification. While these methods are tested on the NuGraph2 application, they can be applied to a broad range of networks, not limited to GNNs. The code for this work is publicly available on GitHub at https://github.com/voetberg/XNuGraph .
Abstract Donor-based spin qubits in silicon are a promising platform for scalable quantum computing due to their long coherence times and high-fidelity gate operations. A viable path for fabricating donor qubit arrays with atomic precision is scanning tunneling microscopy hydroge…
Abstract Physics-informed neural networks (PINNs) face an empirical accuracy floor of approximately O(10^-2) when applied to stiff multiphysics problems like electrochemical transport. This limitation stems from the "PINN trilemma": Monte Carlo collocation, serial automatic diffe…
Abstract The application of quantum reinforcement learning (QRL) to real-time control systems faces significant challenges regarding hardware latency, noise susceptibility, and learning convergence. This work presents an end-to-end investigation of a minimal hybrid quantum-classi…
Abstract Graphs provide a powerful framework for modeling complex systems, but their structural variability poses significant challenges for analysis and classification. To address these challenges, we introduce GAUDI (Graph Autoencoder Uncovering Descriptive Information), a nove…
Abstract Parameter identification in nonlinear dynamical systems is complicated by model-form uncertainty arising from systematic biases that violate the zero-mean error assumption of standard data assimilation methods. Recent neural-network-based approaches learn arbitrary bias…
Abstract We introduce adaptive-basis physics-informed neural networks (AB-PINNs), an adaptive domain decomposition framework for PINNs in which learnable subdomains dynamically evolve during training to align with intrinsic features of the unknown solution. Local networks capture…