Inverse deep learning methods and benchmarks for artificial electromagnetic material design

• URL: http://arxiv.org/abs/2112.10254v1
• Date: Sun, 19 Dec 2021 20:44:53 GMT
• Title: Inverse deep learning methods and benchmarks for artificial electromagnetic material design
• Authors: Simiao Ren, Ashwin Mahendra, Omar Khatib, Yang Deng, Willie J. Padilla and Jordan M. Malof
• Abstract summary: We present a survey of deep learning inverse methods and invertible and conditional invertible neural networks to AEM design. Our methodology is guided by constraints on repeated simulation and an easily integrated metric. We show that as the problem becomes increasingly ill-posed, the neural adjoint with boundary loss (NA) generates better solutions faster.
• Score: 8.47539037890124
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning (DL) inverse techniques have increased the speed of artificial electromagnetic material (AEM) design and improved the quality of resulting devices. Many DL inverse techniques have succeeded on a number of AEM design tasks, but to compare, contrast, and evaluate assorted techniques it is critical to clarify the underlying ill-posedness of inverse problems. Here we review state-of-the-art approaches and present a comprehensive survey of deep learning inverse methods and invertible and conditional invertible neural networks to AEM design. We produce easily accessible and rapidly implementable AEM design benchmarks, which offers a methodology to efficiently determine the DL technique best suited to solving different design challenges. Our methodology is guided by constraints on repeated simulation and an easily integrated metric, which we propose expresses the relative ill-posedness of any AEM design problem. We show that as the problem becomes increasingly ill-posed, the neural adjoint with boundary loss (NA) generates better solutions faster, regardless of simulation constraints. On simpler AEM design tasks, direct neural networks (NN) fare better when simulations are limited, while geometries predicted by mixture density networks (MDN) and conditional variational auto-encoders (VAE) can improve with continued sampling and re-simulation.

Related papers

• Adaptive Swarm Mesh Refinement using Deep Reinforcement Learning with Local Rewards [12.455977048107671]
  Adaptive Mesh Refinement (AMR) improves the Finite Element Method (FEM) We formulate AMR as a system of collaborating, homogeneous agents that iteratively split into multiple new agents. Our approach, Adaptive Swarm Mesh Refinement (ASMR), offers efficient, stable optimization and generates highly adaptive meshes at user-defined resolution during inference.
  arXiv  Detail & Related papers  (2024-06-12T17:26:54Z)
• Large-Scale OD Matrix Estimation with A Deep Learning Method [70.78575952309023]
  The proposed method integrates deep learning and numerical optimization algorithms to infer matrix structure and guide numerical optimization. We conducted tests to demonstrate the good generalization performance of our method on a large-scale synthetic dataset.
  arXiv  Detail & Related papers  (2023-10-09T14:30:06Z)
• Diffusion Generative Inverse Design [28.04683283070957]
  Inverse design refers to the problem of optimizing the input of an objective function in order to enact a target outcome. Recent developments in learned graph neural networks (GNNs) can be used for accurate, efficient, differentiable estimation of simulator dynamics. We show how denoising diffusion diffusion models can be used to solve inverse design problems efficiently and propose a particle sampling algorithm for further improving their efficiency.
  arXiv  Detail & Related papers  (2023-09-05T08:32:07Z)
• A Multi-Head Ensemble Multi-Task Learning Approach for Dynamical Computation Offloading [62.34538208323411]
  We propose a multi-head ensemble multi-task learning (MEMTL) approach with a shared backbone and multiple prediction heads (PHs) MEMTL outperforms benchmark methods in both the inference accuracy and mean square error without requiring additional training data.
  arXiv  Detail & Related papers  (2023-09-02T11:01:16Z)
• Differentiable Multi-Fidelity Fusion: Efficient Learning of Physics Simulations with Neural Architecture Search and Transfer Learning [1.0024450637989093]
  We propose the differentiable mf (DMF) model, which leverages neural architecture search (NAS) to automatically search the suitable model architecture for different problems. DMF can efficiently learn the physics simulations with only a few high-fidelity training samples, and outperform the state-of-the-art methods with a significant margin.
  arXiv  Detail & Related papers  (2023-06-12T07:18:13Z)
• Swarm Reinforcement Learning For Adaptive Mesh Refinement [11.201100158465394]
  We develop a novel AMR that learns reliable, scalable, and efficient refinement strategies on a set of challenging problems. Our approach significantly speeds up, achieving up to 30-fold improvement compared to uniform refinements in complex simulations.
  arXiv  Detail & Related papers  (2023-04-03T09:07:17Z)
• Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach [81.5261621619557]
  We propose an adaptive anomaly detection scheme with hierarchical edge computing (HEC) We first construct multiple anomaly detection DNN models with increasing complexity, and associate each of them to a corresponding HEC layer. Then, we design an adaptive model selection scheme that is formulated as a contextual-bandit problem and solved by using a reinforcement learning policy network.
  arXiv  Detail & Related papers  (2021-08-09T08:45:47Z)
• Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with Heterogeneous Learning Tasks [53.1636151439562]
  Mobile edge computing (MEC) provides a natural platform for AI applications. We present an infrastructure to perform machine learning tasks at an MEC with the assistance of a reconfigurable intelligent surface (RIS) Specifically, we minimize the learning error of all participating users by jointly optimizing transmit power of mobile users, beamforming vectors of the base station, and the phase-shift matrix of the RIS.
  arXiv  Detail & Related papers  (2020-12-25T07:08:50Z)
• Deep Multi-Task Learning for Cooperative NOMA: System Design and Principles [52.79089414630366]
  We develop a novel deep cooperative NOMA scheme, drawing upon the recent advances in deep learning (DL) We develop a novel hybrid-cascaded deep neural network (DNN) architecture such that the entire system can be optimized in a holistic manner.
  arXiv  Detail & Related papers  (2020-07-27T12:38:37Z)
• Deep unfolding of the weighted MMSE beamforming algorithm [9.518010235273783]
  We propose the novel application of deep unfolding to the WMMSE algorithm for a MISO downlink channel. Deep unfolding naturally incorporates expert knowledge, with the benefits of immediate and well-grounded architecture selection, fewer trainable parameters, and better explainability. By means of simulations, we show that, in most of the settings, the unfolded WMMSE outperforms or performs equally to the WMMSE for a fixed number of iterations.
  arXiv  Detail & Related papers  (2020-06-15T14:51:20Z)
• Iterative Algorithm Induced Deep-Unfolding Neural Networks: Precoding Design for Multiuser MIMO Systems [59.804810122136345]
  We propose a framework for deep-unfolding, where a general form of iterative algorithm induced deep-unfolding neural network (IAIDNN) is developed. An efficient IAIDNN based on the structure of the classic weighted minimum mean-square error (WMMSE) iterative algorithm is developed. We show that the proposed IAIDNN efficiently achieves the performance of the iterative WMMSE algorithm with reduced computational complexity.
  arXiv  Detail & Related papers  (2020-06-15T02:57:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.