Obstacle segmentation based on the wave equation and deep learning

Kahana, Adar; Turkel, Eli; Dekel, Shai; Givoli, Dan

doi:10.1016/j.jcp.2020.109458

Cited by 26 publications

(10 citation statements)

References 16 publications

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“…When the classifier is trained, it can be used to start the recognition process. The [1] - [2] and [6] - [16] are applied to target recognition task after the classifier is trained.It can be used for video or image recognition of one or more people. Different sets of [5]python scripts are provided to run different types of recognition of people seen from footage or images.…”

Section: Recognition System Algorithm Design 21 Database Creationmentioning

confidence: 99%

Research on obstacle recognition technology based on optimized deep learning algorithm

Zhou¹,

Meng²,

Huang³

et al. 2023

International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023)

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Section: Recognition System Algorithm Design 21 Database Creationmentioning

confidence: 99%

Research on obstacle recognition technology based on optimized deep learning algorithm

Zhou¹,

Meng²,

Huang³

et al. 2023

International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023)

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“…Goswami et al [12] proposed a PINN algorithm to solve brittle fracture problems and optimize its loss by decreasing the variational energy of the system. Many existing studies have indicated that an NN with physical constraints contributes to improving prediction accuracy [50,33,28,25,45], discovering PDEs and governing equations [10,61], modeling inverse problems [43,26], and solving uncertainty quantification [58,59]. Some other studies also focus on considering general deep learning framework to learn diverse continuous nonlinear operators [32,31,30].…”

Section: Literature Reviewmentioning

confidence: 99%

PCNN: A physics-constrained neural network for multiphase flows

Zheng,

Huang,

Lin

2021

Preprint

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The present study develops a physics-constrained neural network (PCNN) to predict sequential patterns and motions of multiphase flows (MPFs), which includes strong interactions among various fluid phases. To predict the order parameters, which locate individual phases, in the future time, the conditional neural processes and long short-term memory (CNP-LSTM) are applied to quickly infer the dynamics of the phases after encoding only a few observations. After that, the multiphase consistent and conservative boundedness mapping algorithm (MCBOM) is implemented to correct the order parameters predicted from CNP-LSTM in order to strictly satisfy the mass conservation, the summation of the volume fractions of the phases to be unity, the consistency of reduction, and the boundedness of the order parameters. Then, the density of the fluid mixture is updated from the corrected order parameters. Finally, the velocity in the future time is predicted by a physics-informed CNP-LSTM (PICNP-LSTM) where conservation of momentum is included in the loss function with the observed density and velocity as the inputs. The proposed PCNN for MPFs sequentially performs (CNP-LSTM)-(MCBOM)-(PICNP-LSTM), which avoids unphysical behaviors of the order parameters, accelerates the convergence, and requires fewer data to make predictions. Numerical experiments demonstrate that the proposed PCNN is capable of predicting MPFs effectively.

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“…Seidl and Rank 15,16 apply the FWI-adjoint method with multiple wave sources to determine the position, dimension and orientation of a structural flaw. Kahana et al 17 devise a deep-learning technique for identifying multiple obstacles in an acoustic medium.…”

Section: Introductionmentioning

confidence: 99%

Single‐field identification of inclusions and cavities in an elastic medium

Rabinovich,

Givoli,

Turkel

2023

Numerical Meth Engineering

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The inverse problem of identifying a small unknown inclusion or cavity in an elastic medium is considered. Important applications are structural damage identification, medical imaging and geophysical exploration; the latter is the focus of the present investigation. It is assumed that the (isotropic but possibly heterogeneous) material properties of the medium are known, except for the presence of a small inclusion of a different material or of a small cavity. The goal is to find the location, size and shape of the inclusion or cavity. Identification is performed using full waveform inversion (FWI) and the adjoint method for the efficient calculation of the gradient of the misfit function. The identification is accomplished by considering a single unknown material‐property field. The inclusion manifests itself in the inverse solution as a local region where the unknown material property becomes significantly different than the known background property. The limiting case of cavity identification requires special treatment in the minimization process to avoid failure, as Bürchner et al. showed empirically for the scalar wave equation. Their ‐scaling approach, whose success is explained mathematically here for the first time, is extended to elastodynamics. The performance of the proposed method is demonstrated via numerical examples, involving two geophysical models: a homogeneous model and the heterogeneous Marmousi model, which is a standard testing model in geophysical research.

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Obstacle segmentation based on the wave equation and deep learning

Cited by 26 publications

References 16 publications

Research on obstacle recognition technology based on optimized deep learning algorithm

Research on obstacle recognition technology based on optimized deep learning algorithm

PCNN: A physics-constrained neural network for multiphase flows

Single‐field identification of inclusions and cavities in an elastic medium

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