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 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.
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.
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
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Non-parametric seismic data recovery with curvelet frames
Felix J. Herrmann;Gilles Hennenfent.
Geophysical Journal International (2008)
Simply denoise: Wavefield reconstruction via jittered undersampling
Gilles Hennenfent;Felix J. Herrmann.
Mitigating local minima in full-waveform inversion by expanding the search space
Tristan van Leeuwen;Felix J. Herrmann.
Geophysical Journal International (2013)
Seismic denoising with nonuniformly sampled curvelets
G. Hennenfent;F.J. Herrmann.
Computing in Science and Engineering (2006)
Curvelet-based seismic data processing : A multiscale and nonlinear approach
Felix J. Herrmann;Deli Wang;Gilles Hennenfent;Peyman P. Moghaddam.
Randomized sampling and sparsity: Getting more information from fewer samples
Felix J. Herrmann.
An Effective Method for Parameter Estimation with PDE Constraints with Multiple Right-Hand Sides
Eldad Haber;Matthias Chung;Felix Herrmann.
Siam Journal on Optimization (2012)
Sparsity- and continuity-promoting seismic image recovery with curvelet frames
Felix J. Herrmann;Peyman Moghaddam;Christiaan C. Stolk.
Applied and Computational Harmonic Analysis (2008)
A penalty method for PDE-constrained optimization in inverse problems
T. van Leeuwen;Felix J. Herrmann.
Inverse Problems (2016)
Non-linear primary-multiple separation with directional curvelet frames
Felix J. Herrmann;Urs Böniger;Dirk Jacob (Eric) Verschuur.
Geophysical Journal International (2007)
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