Abstract The uniformity of the film thickness of large-aperture mirror is a critical factor affecting the imaging quality of reflective optical systems. A deep learning-based mask design strategy is proposed to reduce this non-uniformity. By developing a convolutional neural network architecture and generating training and validation datasets based on engineering experience, the model can produce a mask that fulfills application requirements following the optimization of network parameters. In comparison to conventional methods, this approach not only markedly decreases the number of experiments but also demonstrates considerable versatility. The trained neural network architecture can be utilized for various vacuum chamber configurations or optical components with diverse surface types, facilitating expedited mask design by merely supplying the relevant dataset. Theoretical results demonstrate that the mask designed using this technology diminishes the film thickness non-uniformity of a large-aperture mirror with a diameter of 3270 mm from 10.56% to 1.7%, so effectively validating its feasibility and superiority.
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…