What is he best known for?
The fields of study he is best known for:
- Mathematical analysis
- Artificial intelligence
- Algorithm
Stanley Osher mainly focuses on Mathematical analysis, Algorithm, Level set method, Applied mathematics and Image processing.
His Mathematical analysis study integrates concerns from other disciplines, such as Regularization and Nonlinear system.
Stanley Osher combines subjects such as Mathematical optimization, Minification and Deblurring with his study of Algorithm.
His Level set method research incorporates elements of Level set, Level set, Curvature, Signed distance function and Vector field.
His research investigates the connection between Applied mathematics and topics such as Scale space that intersect with issues in Inverse.
The concepts of his Image processing study are interwoven with issues in Bounded variation and Pattern recognition.
His most cited work include:
- Nonlinear total variation based noise removal algorithms (11267 citations)
- Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations (11217 citations)
- Efficient implementation of essentially non-oscillatory shock-capturing schemes,II (4161 citations)
What are the main themes of his work throughout his whole career to date?
Stanley Osher mostly deals with Mathematical analysis, Algorithm, Artificial intelligence, Applied mathematics and Computer vision.
The study of Mathematical analysis is intertwined with the study of Level set method in a number of ways.
Stanley Osher interconnects Level set and Level set in the investigation of issues within Level set method.
His Algorithm study combines topics in areas such as Mathematical optimization, Minification and Inverse problem.
His studies link Pattern recognition with Artificial intelligence.
His Applied mathematics study frequently draws parallels with other fields, such as Gradient descent.
He most often published in these fields:
- Mathematical analysis (29.25%)
- Algorithm (23.21%)
- Artificial intelligence (17.74%)
What were the highlights of his more recent work (between 2017-2021)?
- Algorithm (23.21%)
- Artificial neural network (4.53%)
- Applied mathematics (16.04%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Algorithm, Artificial neural network, Applied mathematics, Artificial intelligence and Gradient descent.
Stanley Osher has included themes like Phase retrieval and Minification in his Algorithm study.
His biological study spans a wide range of topics, including Function, Training set, Mathematical optimization and Robustness.
His Applied mathematics study incorporates themes from Dimension, Laplacian smoothing, Partial differential equation, Interpolation and Point.
His studies in Artificial intelligence integrate themes in fields like Machine learning, Residual and Pattern recognition.
His Regularization research is multidisciplinary, relying on both Smoothing and Image processing.
Between 2017 and 2021, his most popular works were:
- A review of level-set methods and some recent applications (144 citations)
- Deep relaxation: partial differential equations for optimizing deep neural networks (62 citations)
- A Parallel Method for Earth Mover’s Distance (52 citations)
In his most recent research, the most cited papers focused on:
- Mathematical analysis
- Artificial intelligence
- Algorithm
Stanley Osher spends much of his time researching Algorithm, Artificial neural network, Applied mathematics, Function and Optimal control.
In the field of Algorithm, his study on Regularization overlaps with subjects such as Quantization.
The study incorporates disciplines such as Saddle point, Structure, Training set and Mean field theory in addition to Artificial neural network.
The Applied mathematics study combines topics in areas such as Stability, Dimension, Metric and Laplace operator.
His study in Optimal control is interdisciplinary in nature, drawing from both Curse of dimensionality, Hamilton–Jacobi equation, Differential game, Viscosity solution and Gaussian noise.
His Mathematical optimization research is multidisciplinary, incorporating elements of Image processing and Type.
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