What is he best known for?
The fields of study he is best known for:
- Algorithm
- Statistics
- Mathematical analysis
Felix J. Herrmann focuses on Algorithm, Mathematical optimization, Compressed sensing, Curvelet and Inverse problem.
His biological study spans a wide range of topics, including Full waveform, Fourier transform, Dimensionality reduction, Noise reduction and Multiple.
Felix J. Herrmann has included themes like Computational complexity theory and Signal processing in his Mathematical optimization study.
His work deals with themes such as Sampling, Sampling and Curse of dimensionality, which intersect with Compressed sensing.
His Curvelet research includes elements of Amplitude, Acoustics and Scaling.
His work carried out in the field of Computer vision brings together such families of science as Noise and Regular grid.
His most cited work include:
- Non-parametric seismic data recovery with curvelet frames (354 citations)
- Simply denoise: Wavefield reconstruction via jittered undersampling (196 citations)
- Seismic denoising with nonuniformly sampled curvelets (174 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Algorithm, Curvelet, Compressed sensing, Regional geology and Mathematical optimization.
His Algorithm study integrates concerns from other disciplines, such as Full waveform, Inverse problem, Interpolation, Geomorphology and Multiple.
His Curvelet research incorporates themes from Amplitude, Subtraction and Thresholding.
His Compressed sensing research is multidisciplinary, incorporating elements of Sampling and Remote sensing.
His Regional geology study combines topics from a wide range of disciplines, such as Economic geology, Engineering geology, Gemology, Petrology and Environmental geology.
Felix J. Herrmann combines subjects such as Computer vision and Pattern recognition with his study of Artificial intelligence.
He most often published in these fields:
- Algorithm (49.86%)
- Curvelet (19.35%)
- Compressed sensing (15.26%)
What were the highlights of his more recent work (between 2018-2021)?
- Algorithm (49.86%)
- Inverse problem (8.45%)
- Convolutional neural network (3.00%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Algorithm, Inverse problem, Convolutional neural network, Overfitting and Posterior probability.
The various areas that he examines in his Algorithm study include Time domain, Matrix, Sampling, Wave equation and Speedup.
His studies deal with areas such as Artificial neural network, Regularization, Mathematical optimization and Inference as well as Inverse problem.
His work carried out in the field of Inference brings together such families of science as Divergence and Compressed sensing.
His Overfitting course of study focuses on Prior probability and Range, Noise, Pattern recognition and Langevin dynamics.
His Uncertainty quantification study combines topics from a wide range of disciplines, such as Deep learning and Artificial intelligence.
Between 2018 and 2021, his most popular works were:
- Devito (v3.1.0): an embedded domain-specific language for finite differences and geophysical exploration (40 citations)
- A large-scale framework for symbolic implementations of seismic inversion algorithms in JuliaA symbolic seismic inversion framework (20 citations)
- Projection methods and applications for seismic nonlinear inverse problems with multiple constraints (20 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Mathematical analysis
- Artificial intelligence
Felix J. Herrmann mainly focuses on Algorithm, Deep learning, Artificial intelligence, Convolutional neural network and Solver.
His multidisciplinary approach integrates Algorithm and Implementation in his work.
His Deep learning research incorporates themes from Acoustics, Reciprocity, Uncertainty quantification, Ocean bottom and Pattern recognition.
His Convolutional neural network study deals with Artificial neural network intersecting with Latent variable and Basis.
His Solver research incorporates elements of Finite difference and Applied mathematics.
His Finite difference study also includes
- Partial differential equation, which have a strong connection to Mathematical optimization,
- Computational science which connect with Discretization.
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