FWIGAN: Full‐Waveform Inversion via a Physics‐Informed Generative Adversarial Network

Yang, Fangshu; Ma, Jianwei

doi:10.1029/2022jb025493

Cited by 28 publications

(2 citation statements)

References 57 publications

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“…GANs can also be applied to inverse problems, as presented in [576] for full waveform inversion. The generator predicts the material distribution, which is used in a differentiable simulation providing the forward solution in the form of a seismogram.…”

Section: Generative Design and Design Optimizationmentioning

confidence: 99%

Deep learning in computational mechanics: a review

Herrmann,

Kollmannsberger

2024

Comput Mech

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The rapid growth of deep learning research, including within the field of computational mechanics, has resulted in an extensive and diverse body of literature. To help researchers identify key concepts and promising methodologies within this field, we provide an overview of deep learning in deterministic computational mechanics. Five main categories are identified and explored: simulation substitution, simulation enhancement, discretizations as neural networks, generative approaches, and deep reinforcement learning. This review focuses on deep learning methods rather than applications for computational mechanics, thereby enabling researchers to explore this field more effectively. As such, the review is not necessarily aimed at researchers with extensive knowledge of deep learning—instead, the primary audience is researchers on the verge of entering this field or those attempting to gain an overview of deep learning in computational mechanics. The discussed concepts are, therefore, explained as simple as possible.

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Section: Generative Design and Design Optimizationmentioning

confidence: 99%

Deep learning in computational mechanics: a review

Herrmann,

Kollmannsberger

2024

Comput Mech

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“…Waves are a fundamental concept in many engineering disciplines. In particular, PINNs have been explored and applied to full waveform inversion (FWI) due to their ability to solve inverse problems with noisy inputs [20][21][22][23]. The wave equation provides a mathematical framework for understanding and predicting how waves propagate through various physical systems.…”

Section: Introductionmentioning

confidence: 99%

Physics-Informed Neural Networks for High-Frequency and Multi-Scale Problems Using Transfer Learning

Mustajab,

Lyu,

Rizvi

et al. 2024

Applied Sciences

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Physics-Informed Neural Network (PINN) is a data-driven solver for partial and ordinary differential equations (ODEs/PDEs). It provides a unified framework to address both forward and inverse problems. However, the complexity of the objective function often leads to training failures. This issue is particularly prominent when solving high-frequency and multi-scale problems. We proposed using transfer learning to boost the robustness and convergence of training PINN, starting training from low-frequency problems and gradually approaching high-frequency problems through fine-tuning. Through two case studies, we discovered that transfer learning can effectively train PINNs to approximate solutions from low-frequency problems to high-frequency problems without increasing network parameters. Furthermore, it requires fewer data points and less training time. We compare the PINN results using direct differences and L2 relative error showing the advantage of using transfer learning techniques. We describe our training strategy in detail, including optimizer selection, and suggest guidelines for using transfer learning to train neural networks to solve more complex problems.

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Generative deep learning for data generation in natural hazard analysis: motivations, advances, challenges, and opportunities

Ma,

Mei,

2024

Artif Intell Rev

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Data mining and analysis are critical for preventing or mitigating natural hazards. However, data availability in natural hazard analysis is experiencing unprecedented challenges due to economic, technical, and environmental constraints. Recently, generative deep learning has become an increasingly attractive solution to these challenges, which can augment, impute, or synthesize data based on these learned complex, high-dimensional probability distributions of data. Over the last several years, much research has demonstrated the remarkable capabilities of generative deep learning for addressing data-related problems in natural hazards analysis. Data processed by deep generative models can be utilized to describe the evolution or occurrence of natural hazards and contribute to subsequent natural hazard modeling. Here we present a comprehensive review concerning generative deep learning for data generation in natural hazard analysis. (1) We summarized the limitations associated with data availability in natural hazards analysis and identified the fundamental motivations for employing generative deep learning as a critical response to these challenges. (2) We discuss several deep generative models that have been applied to overcome the problems caused by limited data availability in natural hazards analysis. (3) We analyze advances in utilizing generative deep learning for data generation in natural hazard analysis. (4) We discuss challenges associated with leveraging generative deep learning in natural hazard analysis. (5) We explore further opportunities for leveraging generative deep learning in natural hazard analysis. This comprehensive review provides a detailed roadmap for scholars interested in applying generative models for data generation in natural hazard analysis.

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FWIGAN: Full‐Waveform Inversion via a Physics‐Informed Generative Adversarial Network

Cited by 28 publications

References 57 publications

Deep learning in computational mechanics: a review

Deep learning in computational mechanics: a review

Physics-Informed Neural Networks for High-Frequency and Multi-Scale Problems Using Transfer Learning

Generative deep learning for data generation in natural hazard analysis: motivations, advances, challenges, and opportunities

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