PACE: Pacing Operator Learning to Accurate Optical Field Simulation for Complicated Photonic Devices

• URL: http://arxiv.org/abs/2411.03527v1
• Date: Tue, 05 Nov 2024 22:03:14 GMT
• Title: PACE: Pacing Operator Learning to Accurate Optical Field Simulation for Complicated Photonic Devices
• Authors: Hanqing Zhu, Wenyan Cong, Guojin Chen, Shupeng Ning, Ray T. Chen, Jiaqi Gu, David Z. Pan,
• Abstract summary: Existing SOTA approaches, NeurOLight, struggle with predicting high-fidelity fields for real-world complicated photonic devices. We propose a novel cross-axis factorized PACE operator with a strong long-distance modeling capacity. Inspired by human learning, we conquer the simulation task for extremely hard cases into two progressively easy tasks.
• Score: 14.671301859745453
• License:
• Abstract: Electromagnetic field simulation is central to designing, optimizing, and validating photonic devices and circuits. However, costly computation associated with numerical simulation poses a significant bottleneck, hindering scalability and turnaround time in the photonic circuit design process. Neural operators offer a promising alternative, but existing SOTA approaches, NeurOLight, struggle with predicting high-fidelity fields for real-world complicated photonic devices, with the best reported 0.38 normalized mean absolute error in NeurOLight. The inter-plays of highly complex light-matter interaction, e.g., scattering and resonance, sensitivity to local structure details, non-uniform learning complexity for full-domain simulation, and rich frequency information, contribute to the failure of existing neural PDE solvers. In this work, we boost the prediction fidelity to an unprecedented level for simulating complex photonic devices with a novel operator design driven by the above challenges. We propose a novel cross-axis factorized PACE operator with a strong long-distance modeling capacity to connect the full-domain complex field pattern with local device structures. Inspired by human learning, we further divide and conquer the simulation task for extremely hard cases into two progressively easy tasks, with a first-stage model learning an initial solution refined by a second model. On various complicated photonic device benchmarks, we demonstrate one sole PACE model is capable of achieving 73% lower error with 50% fewer parameters compared with various recent ML for PDE solvers. The two-stage setup further advances high-fidelity simulation for even more intricate cases. In terms of runtime, PACE demonstrates 154-577x and 11.8-12x simulation speedup over numerical solver using scipy or highly-optimized pardiso solver, respectively. We open sourced the code and dataset.

Related papers

• PIC2O-Sim: A Physics-Inspired Causality-Aware Dynamic Convolutional Neural Operator for Ultra-Fast Photonic Device FDTD Simulation [18.832901682895944]
  We introduce a physics-inspired AI-based prediction framework PIC2OSim for photonic device simulation. We show that PIC2O-Sim provides 51.2% lower roll-out prediction error, 23.5 times fewer parameters than state-of-the-art neural operators.
  arXiv  Detail & Related papers  (2024-06-24T18:15:36Z)
• Neural Operators for Accelerating Scientific Simulations and Design [85.89660065887956]
  An AI framework, known as Neural Operators, presents a principled framework for learning mappings between functions defined on continuous domains. Neural Operators can augment or even replace existing simulators in many applications, such as computational fluid dynamics, weather forecasting, and material modeling.
  arXiv  Detail & Related papers  (2023-09-27T00:12:07Z)
• Reduced Simulations for High-Energy Physics, a Middle Ground for Data-Driven Physics Research [0.0]
  Subatomic particle track reconstruction is a vital task in High-Energy Physics experiments. We provide the REDuced VIrtual Detector (REDVID) as a complexity-reduced detector model and particle collision event simulator combo.
  arXiv  Detail & Related papers  (2023-08-30T12:50:45Z)
• Complexity Matters: Rethinking the Latent Space for Generative Modeling [65.64763873078114]
  In generative modeling, numerous successful approaches leverage a low-dimensional latent space, e.g., Stable Diffusion. In this study, we aim to shed light on this under-explored topic by rethinking the latent space from the perspective of model complexity.
  arXiv  Detail & Related papers  (2023-07-17T07:12:29Z)
• Learning Controllable Adaptive Simulation for Multi-resolution Physics [86.8993558124143]
  We introduce Learning controllable Adaptive simulation for Multi-resolution Physics (LAMP) as the first full deep learning-based surrogate model. LAMP consists of a Graph Neural Network (GNN) for learning the forward evolution, and a GNN-based actor-critic for learning the policy of spatial refinement and coarsening. We demonstrate that our LAMP outperforms state-of-the-art deep learning surrogate models, and can adaptively trade-off computation to improve long-term prediction error.
  arXiv  Detail & Related papers  (2023-05-01T23:20:27Z)
• NeuralStagger: Accelerating Physics-constrained Neural PDE Solver with Spatial-temporal Decomposition [67.46012350241969]
  This paper proposes a general acceleration methodology called NeuralStagger. It decomposing the original learning tasks into several coarser-resolution subtasks. We demonstrate the successful application of NeuralStagger on 2D and 3D fluid dynamics simulations.
  arXiv  Detail & Related papers  (2023-02-20T19:36:52Z)
• NeurOLight: A Physics-Agnostic Neural Operator Enabling Parametric Photonic Device Simulation [17.295318670037886]
  A physics-agnostic neural framework, dubbed NeurOLight, is proposed to learn a family of frequency-domain Maxwell PDEs for ultra-fast parametric photonic device simulation. We show that NeurOLight generalizes to a large space of unseen simulation settings, demonstrates 2-orders-of-magnitude faster simulation speed than numerical solvers, and outperforms prior neural network models by 54% lower prediction error with 44% fewer parameters.
  arXiv  Detail & Related papers  (2022-09-19T21:25:26Z)
• Multi-fidelity Hierarchical Neural Processes [79.0284780825048]
  Multi-fidelity surrogate modeling reduces the computational cost by fusing different simulation outputs. We propose Multi-fidelity Hierarchical Neural Processes (MF-HNP), a unified neural latent variable model for multi-fidelity surrogate modeling. We evaluate MF-HNP on epidemiology and climate modeling tasks, achieving competitive performance in terms of accuracy and uncertainty estimation.
  arXiv  Detail & Related papers  (2022-06-10T04:54:13Z)
• Accelerating Part-Scale Simulation in Liquid Metal Jet Additive Manufacturing via Operator Learning [0.0]
  Part-scale predictions require many small-scale simulations. A model describing droplet coalescence for LMJ may include coupled incompressible fluid flow, heat transfer, and phase change equations. We apply an operator learning approach to learn a mapping between initial and final states of the droplet coalescence process.
  arXiv  Detail & Related papers  (2022-02-02T17:24:16Z)
• Edge Federated Learning Via Unit-Modulus Over-The-Air Computation (Extended Version) [64.76619508293966]
  This paper proposes a unit-modulus over-the-air computation (UM-AirComp) framework to facilitate efficient edge federated learning. It uploads simultaneously local model parameters and updates global model parameters via analog beamforming. We demonstrate the implementation of UM-AirComp in a vehicle-to-everything autonomous driving simulation platform.
  arXiv  Detail & Related papers  (2021-01-28T15:10:22Z)
• Federated Learning via Intelligent Reflecting Surface [30.935389187215474]
  Over-the-air computation algorithm (AirComp) based learning (FL) is capable of achieving fast model aggregation by exploiting the waveform superposition property of multiple access channels. In this paper, we propose a two-step optimization framework to achieve fast yet reliable model aggregation for AirComp-based FL. Simulation results will demonstrate that our proposed framework and the deployment of an IRS can achieve a lower training loss and higher FL prediction accuracy than the baseline algorithms.
  arXiv  Detail & Related papers  (2020-11-10T11:29:57Z)

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.