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Ghattas, Omar

  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 · 15 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. 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 · 10 citations
    Computer Science · Decision Sciences · #Advanced Multi-Objective Optimization Algorithms #Gaussian Processes and Bayesian Inference #Optimal Experimental Design Methods
  3. 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, James Martin, Petra, Noemi +5 · 7 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)
  4. Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning
    2022/06/21 by Thomas O’Leary-Roseberry, Peng Chen, O'Leary-Roseberry, Thomas +5 · 10 citations
    Physics and Astronomy · Computer Science · Materials Science · #Model Reduction and Neural Networks #Neural Networks and Applications #Machine Learning in Materials Science
  5. Derivative-Informed Projected Neural Networks for High-Dimensional\n Parametric Maps Governed by PDEs
    2020/11/30 by Thomas O’Leary-Roseberry, Umberto Villa, O'Leary-Roseberry, Thomas +5 · 10 citations
    Physics and Astronomy · Decision Sciences · #Model Reduction and Neural Networks #Probabilistic and Robust Engineering Design
  6. 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
  7. Projected Stein Variational Gradient Descent
    2020/02/09 by Peng Chen, Chen, Peng, Omar Ghattas +1 · 5 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
  8. 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 · 3 citations
    Computer Science · Mathematics · #Gaussian Processes and Bayesian Inference #Statistical and numerical algorithms #Advanced Multi-Objective Optimization Algorithms
  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, Noémi Petra, Alexanderian, Alen +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. hIPPYlib-MUQ: A Bayesian Inference Software Framework for Integration of Data with Complex Predictive Models under Uncertainty
    2021/12/01 by Kim, Ki-Tae, Villa, Umberto, Parno, Matthew +3 · 3 citations
    #35Q62 #35Q93 #35R30 #49M15 #62F15 #65C99 #65K10 #68N99 #Computation (stat.CO) #FOS: Computer and information sciences #FOS: Mathematics #Mathematical Software (cs.MS) #Numerical Analysis (math.NA) #Optimization and Control (math.OC)
  11. On Bayesian A- and D-optimal experimental designs in infinite dimensions
    2014/08/27 by Alexanderian, Alen, Gloor, Philip, Ghattas, Omar · 2 citations
    #46N30 #49N45 #62F15 #62K05 #FOS: Mathematics #Statistics Theory (math.ST)
  12. 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
  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. Stein variational reduced basis Bayesian inversion
    2020/02/25 by Peng Chen, Omar Ghattas, Chen, Peng +1 · 2 citations
    Decision Sciences · Engineering · Physics and Astronomy · #FOS: Mathematics #Model Reduction and Neural Networks #Nuclear reactor physics and engineering #Numerical Analysis (math.NA) #Probabilistic and Robust Engineering Design
  16. Efficient PDE-Constrained optimization under high-dimensional uncertainty using derivative-informed neural operators
    2023/05/31 by Dingcheng Luo, Thomas O’Leary-Roseberry, Luo, Dingcheng +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
  17. Research and Education in Computational Science and Engineering
    2016/10/09 by Rüde, Ulrich, Willcox, Karen, McInnes, Lois Curfman +30 · 1 citation
    #00A72 #62-07 #68U20 #68W01 #68W10 #97A99 #97M10 #97N80 #97R20 #97R30 #Computational Engineering #FOS: Computer and information sciences #FOS: Mathematics #Finance #G.0 #G.4 #History and Overview (math.HO) #I.6 #J.0 #J.2 #J.3 #J.4 #J.6 #J.7 #K.3.2 #Other Statistics (stat.OT) #and Science (cs.CE)
  18. 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 · 2 citations
    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)
  19. Inexact Newton Methods for Stochastic Nonconvex Optimization with Applications to Neural Network Training
    2019/05/16 by O'Leary-Roseberry, Thomas, Alger, Nick, Ghattas, Omar · 1 citation
    #FOS: Mathematics #Numerical Analysis (math.NA) #Optimization and Control (math.OC)
  20. Tensor train construction from tensor actions, with application to\n compression of large high order derivative tensors
    2020/02/14 by Nick Alger, Peng Chen, Alger, Nick +3 · 1 citation
    Computer Science · Mathematics · Physics and Astronomy · #Computational Physics and Python Applications #Tensor decomposition and applications #Model Reduction and Neural Networks
  21. Taylor approximation for chance constrained optimization problems governed by partial differential equations with high-dimensional random parameters
    2020/11/19 by Chen, Peng, Ghattas, Omar · 1 citation
    #Computation (stat.CO) #FOS: Computer and information sciences #FOS: Mathematics #Optimization and Control (math.OC)
  22. Inference of Heterogeneous Material Properties via Infinite-Dimensional Integrated DIC
    2024/07/22 by Joseph G. Kirchhoff, Dingcheng Luo, Kirchhoff, Joseph +5 · 2 citations
    Engineering · #Advancements in Photolithography Techniques #FOS: Mathematics #Numerical Analysis (math.NA) #Optimization and Control (math.OC) #Semiconductor materials and devices
  23. Bayesian model calibration for diblock copolymer thin film self-assembly using power spectrum of microscopy data and machine learning surrogate
    2023/06/08 by Cao, Lianghao, Wu, Keyi, Oden, J. Tinsley +2 · 1 citation
    #Computation (stat.CO) #Computational Physics (physics.comp-ph) #FOS: Computer and information sciences #FOS: Mathematics #FOS: Physical sciences #Materials Science (cond-mat.mtrl-sci) #Numerical Analysis (math.NA)
  24. A fast and scalable computational framework for large-scale and high-dimensional Bayesian optimal experimental design
    2020/10/28 by Keyi Wu, Peng Chen, Wu, Keyi +3 · 1 citation
    Computer Science · Decision Sciences · #Gaussian Processes and Bayesian Inference #Advanced Multi-Objective Optimization Algorithms #Probabilistic and Robust Engineering Design
  25. A data scalable augmented Lagrangian KKT preconditioner for large scale inverse problems
    2016/07/13 by Alger, Nick, Villa, Umberto, Bui-Thanh, Tan +1 · 1 citation
    #49K20 #65F08 #65F22 #65J22 #65K10 #65N21 #FOS: Mathematics #Numerical Analysis (math.NA)
  26. 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)
  27. Goal-Oriented Real-Time Bayesian Inference for Linear Autonomous Dynamical Systems With Application to Digital Twins for Tsunami Early Warning
    2025/01/24 by Henneking, Stefan, Venkat, Sreeram, Ghattas, Omar · 3 citations
    #FOS: Mathematics #Numerical Analysis (math.NA)
  28. 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
  29. Point spread function approximation of high rank Hessians with locally supported non-negative integral kernels
    2023/07/07 by Nick Alger, Alger, Nick, Tucker Hartland +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)