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Strong Solutions for PDE-Based Tomography by Unsupervised Learning
Leah Bar
,
Nir Sochen
School of Mathematical Sciences
Tel Aviv University
Research output
:
Contribution to journal
›
Article
›
peer-review
29
Scopus citations
Overview
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Dive into the research topics of 'Strong Solutions for PDE-Based Tomography by Unsupervised Learning'. Together they form a unique fingerprint.
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Keyphrases
Tomography
100%
Inverse Problem
100%
Unsupervised Learning
100%
PDE
100%
Strong Solution
100%
Neural Network
50%
Forward Problem
50%
Regularizer
50%
Problem Model
50%
Semi-inverse
50%
Two Dimensional
25%
Differentiable Function
25%
High-order
25%
Weak Sense
25%
Numerical Methods
25%
Value Function
25%
Loss Function
25%
Finite Element
25%
Analytical Form
25%
Differential Operator
25%
Finite Difference
25%
L2 Norm
25%
Unsupervised Method
25%
Condition Constraint
25%
Fidelity Term
25%
Gridless Method
25%
Freeform Shape
25%
Diffusion Equation
25%
Free-free
25%
L-norm
25%
Any Order
25%
Second-order Systems
25%
Arbitrary Domains
25%
Nonlinear PDEs
25%
Electrical Impedance Tomography
25%
Meshless
25%
PDE Solvers
25%
Shape-free
25%
Mesh Shape
25%
PDE Solutions
25%
Mathematics
Partial Differential Equation
100%
Strong Solution
100%
Neural Network
40%
Boundary Condition
40%
Initial Condition
20%
Minimizes
20%
Pointwise
20%
Differentiable Function
20%
Differential Operator
20%
Function Value
20%
Forward Problem
20%
Diffusion Equation
20%
Loss Function
20%
Set Point
20%
Finite Element Method
20%
Finite Difference Method
20%
Mathematical Method
20%
Engineering
Strong Solution
100%
Partial Differential Equation
100%
Model Parameter
80%
Two Dimensional
20%
Numerical Methods
20%
Diffusion Equation
20%
Set Point
20%
Value Function
20%
Second-Order System
20%
Loss Function
20%
Electrical Impedance
20%
Initial and Boundary Condition
20%
Finite Element Analysis
20%
Boundary Condition
20%
Material Science
Tomography
100%
Finite Element Method
33%
Electrical Impedance
33%
Finite Difference Method
33%