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Omar Ghattas

  1. A computational framework for infinite-dimensional Bayesian inverse problems. Part I: The linearized case, with application to global seismic inversion
    2013/08/06 by Tan Bui–Thanh, Bui-Thanh, Tan, Omar Ghattas +5 · 11 citations
    Computer Science · Engineering · Mathematics · #35L05 #35Q62 #35Q93 #35R30 #62F15 #65C60 #Computation (stat.CO) #FOS: Computer and information sciences #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Markov Chains and Monte Carlo Methods #Methodology (stat.ME) #Numerical Analysis (math.NA) #Optimization and Control (math.OC) #Sparse and Compressive Sensing Techniques
  2. Model Reduction for Large-Scale Systems with High-Dimensional Parametric Input Space
    2008/01/01 by Tan Bui–Thanh, T. Bui-Thanh, K. Willcox +3 · 8 citations
    Computer Science · Decision Sciences · Physics and Astronomy · #Advanced Multi-Objective Optimization Algorithms #Model Reduction and Neural Networks #Probabilistic and Robust Engineering Design
  3. A-optimal design of experiments for infinite-dimensional Bayesian linear\n inverse problems with regularized \ℓ0-sparsification
    2013/08/19 by Alen Alexanderian, Noémi Petra, Alexanderian, Alen +5 · 7 citations
    Computer Science · Decision Sciences · #Advanced Multi-Objective Optimization Algorithms #Gaussian Processes and Bayesian Inference #Optimal Experimental Design Methods
  4. A computational framework for infinite-dimensional Bayesian inverse\n problems: Part II. Stochastic Newton MCMC with application to ice sheet flow\n inverse problems
    2013/08/28 by Noémi Petra, Petra, Noemi, James Martin +5 · 6 citations
    Computer Science · Mathematics · #35Q62 #35Q93 #35R30 #49M15 #62F15 #65C40 #65C60 #86A40 #Computation (stat.CO) #FOS: Computer and information sciences #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Markov Chains and Monte Carlo Methods #Methodology (stat.ME) #Numerical Analysis (math.NA) #Optimization and Control (math.OC) #Statistical Methods and Inference #Statistics Theory (math.ST)
  5. Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning
    2022/06/21 by Thomas O’Leary-Roseberry, O'Leary-Roseberry, Thomas, Peng Chen +5 · 9 citations
    Physics and Astronomy · Computer Science · Materials Science · #Model Reduction and Neural Networks #Neural Networks and Applications #Machine Learning in Materials Science
  6. Derivative-Informed Projected Neural Networks for High-Dimensional\n Parametric Maps Governed by PDEs
    2020/11/30 by Thomas O’Leary-Roseberry, O'Leary-Roseberry, Thomas, Umberto Villa +5 · 8 citations
    Physics and Astronomy · Decision Sciences · #Model Reduction and Neural Networks #Probabilistic and Robust Engineering Design
  7. Large-scale Bayesian optimal experimental design with derivative-informed projected neural network
    2022/01/20 by Keyi Wu, Wu, Keyi, Thomas O’Leary-Roseberry +5 · 6 citations
    Computer Science · Decision Sciences · #Advanced Multi-Objective Optimization Algorithms #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Numerical Analysis (math.NA) #Optimization and Control (math.OC) #Probabilistic and Robust Engineering Design
  8. Projected Stein Variational Gradient Descent
    2020/02/09 by Peng Chen, Omar Ghattas, Chen, Peng +1 · 3 citations
    Computer Science · Mathematics · Medicine · #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Markov Chains and Monte Carlo Methods #Medical Imaging Techniques and Applications
  9. A Fast and Scalable Method for A-Optimal Design of Experiments for\n Infinite-dimensional Bayesian Nonlinear Inverse Problems
    2014/10/21 by Alen Alexanderian, Alexanderian, Alen, Noémi Petra +5 · 2 citations
    Computer Science · Decision Sciences · #35Q62 #35Q93 #35R30 #62F15 #62K05 #65C60 #Advanced Multi-Objective Optimization Algorithms #Computation (stat.CO) #FOS: Computer and information sciences #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Methodology (stat.ME) #Optimal Experimental Design Methods #Optimization and Control (math.OC)
  10. Derivative-informed neural operator acceleration of geometric MCMC for infinite-dimensional Bayesian inverse problems
    2024/03/13 by Lianghao Cao, Cao, Lianghao, Thomas O’Leary-Roseberry +3 · 4 citations
    Computer Science · #Computation (stat.CO) #FOS: Computer and information sciences #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications #Numerical Analysis (math.NA) #Target Tracking and Data Fusion in Sensor Networks
  11. hIPPYlib: An Extensible Software Framework for Large-Scale Inverse\n Problems Governed by PDEs; Part I: Deterministic Inversion and Linearized\n Bayesian Inference
    2019/09/09 by Umberto Villa, Noémi Petra, Villa, Umberto +3 · 2 citations
    Computer Science · Mathematics · #Gaussian Processes and Bayesian Inference #Statistical and numerical algorithms #Advanced Multi-Objective Optimization Algorithms
  12. hIPPYlib
    2021/04/01 by Umberto Villa, Noemi Petra, Noémi Petra +1 · 3 citations
    Computer Science · Decision Sciences · Environmental Science · #Gaussian Processes and Bayesian Inference #Probabilistic and Robust Engineering Design #Soil Geostatistics and Mapping
  13. An efficient method for goal-oriented linear Bayesian optimal experimental design: Application to optimal sensor placemen
    2021/02/12 by Keyi Wu, Peng Chen, Wu, Keyi +3 · 2 citations
    Computer Science · Decision Sciences · #Advanced Multi-Objective Optimization Algorithms #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Numerical Analysis (math.NA) #Optimal Experimental Design Methods #Optimization and Control (math.OC)
  14. Hierarchical Matrix Approximations of Hessians Arising in Inverse\n Problems Governed by PDEs
    2020/03/23 by Ilona Ambartsumyan, Wajih Boukaram, Ambartsumyan, Ilona +13 · 2 citations
    Physics and Astronomy · Engineering · Mathematics · #NMR spectroscopy and applications #Sparse and Compressive Sensing Techniques #Numerical methods in inverse problems
  15. Efficient PDE-Constrained optimization under high-dimensional uncertainty using derivative-informed neural operators
    2023/05/31 by Dingcheng Luo, Luo, Dingcheng, Thomas O’Leary-Roseberry +5 · 2 citations
    Decision Sciences · Engineering · Physics and Astronomy · #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Model Reduction and Neural Networks #Numerical Analysis (math.NA) #Optimization and Control (math.OC) #Probabilistic and Robust Engineering Design #Reservoir Engineering and Simulation Methods
  16. Projected Stein Variational Newton: A Fast and Scalable Bayesian Inference Method in High Dimensions
    2019/01/24 by Peng Chen, Keyi Wu, Chen, Peng +7 · 1 citation
    Computer Science · Mathematics · #Bayesian Methods and Mixture Models #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Markov Chains and Monte Carlo Methods #Optimization and Control (math.OC)
  17. Tensor train construction from tensor actions, with application to\n compression of large high order derivative tensors
    2020/02/14 by Nick Alger, Alger, Nick, Peng Chen +3 · 1 citation
    Computer Science · Mathematics · Physics and Astronomy · #Computational Physics and Python Applications #Tensor decomposition and applications #Model Reduction and Neural Networks
  18. A fast and scalable computational framework for large-scale and high-dimensional Bayesian optimal experimental design
    2020/10/28 by Keyi Wu, Wu, Keyi, Peng Chen +3 · 1 citation
    Computer Science · Decision Sciences · #Gaussian Processes and Bayesian Inference #Advanced Multi-Objective Optimization Algorithms #Probabilistic and Robust Engineering Design
  19. Inference of Heterogeneous Material Properties via Infinite-Dimensional Integrated DIC
    2024/07/22 by Joseph G. Kirchhoff, Dingcheng Luo, Kirchhoff, Joseph +5 · 1 citation
    Engineering · #Advancements in Photolithography Techniques #FOS: Mathematics #Numerical Analysis (math.NA) #Optimization and Control (math.OC) #Semiconductor materials and devices
  20. Scalable matrix-free adaptive product-convolution approximation for\n locally translation-invariant operators
    2018/05/15 by Nick Alger, Vishwas Rao, Alger, Nick +7 · 1 citation
    Engineering · Physics and Astronomy · #Advanced Numerical Methods in Computational Mathematics #Electromagnetic Scattering and Analysis #Electromagnetic Simulation and Numerical Methods #FOS: Mathematics #Numerical Analysis (math.NA)
  21. Real-Time Bayesian Inference at Extreme Scale: A Digital Twin for Tsunami Early Warning Applied to the Cascadia Subduction Zone
    2025/04/23 by Stefan Henneking, Sreeram Venkat, Henneking, Stefan +15 · 1 voice · 2 citations
    Computer Science · Earth and Planetary Sciences · #Seismology and Earthquake Studies #Seismic Waves and Analysis #earthquake and tectonic studies
  22. Point spread function approximation of high rank Hessians with locally supported non-negative integral kernels
    2023/07/07 by Nick Alger, Tucker Hartland, Alger, Nick +5 · 1 citation
    Computer Science · Mathematics · Physics and Astronomy · #Advanced Mathematical Modeling in Engineering #Black Holes and Theoretical Physics #FOS: Mathematics #Geometric Analysis and Curvature Flows #Numerical Analysis (math.NA)