George Em Karniadakis
Orcid: 0000-0002-9713-7120Affiliations:
- Brown University, Division of Applied Mathematics, Providence, RI, USA
According to our database1,
George Em Karniadakis
authored at least 332 papers
between 1996 and 2024.
Collaborative distances:
Collaborative distances:
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Online presence:
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on orcid.org
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on id.loc.gov
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on d-nb.info
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Bibliography
2024
NeuralUQ: A Comprehensive Library for Uncertainty Quantification in Neural Differential Equations and Operators.
SIAM Rev., February, 2024
IEEE Trans. Neural Networks Learn. Syst., January, 2024
Real-time prediction of gas flow dynamics in diesel engines using a deep neural operator framework.
Appl. Intell., January, 2024
Leveraging Multitime Hamilton-Jacobi PDEs for Certain Scientific Machine Learning Problems.
SIAM J. Sci. Comput., 2024
Bridging scales in multiscale bubble growth dynamics with correlated fluctuations using neural operator learning.
CoRR, 2024
CoRR, 2024
Score-Based Physics-Informed Neural Networks for High-Dimensional Fokker-Planck Equations.
CoRR, 2024
DeepOnet Based Preconditioning Strategies For Solving Parametric Linear Systems of Equations.
CoRR, 2024
Analysis of biologically plausible neuron models for regression with spiking neural networks.
CoRR, 2024
2023
A combined computational and experimental investigation of the filtration function of splenic macrophages in sickle cell disease.
PLoS Comput. Biol., December, 2023
Learning Poisson Systems and Trajectories of Autonomous Systems via Poisson Neural Networks.
IEEE Trans. Neural Networks Learn. Syst., November, 2023
Instability-wave prediction in hypersonic boundary layers with physics-informed neural operators.
J. Comput. Sci., November, 2023
A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions.
J. Comput. Phys., November, 2023
J. Comput. Phys., November, 2023
J. Comput. Phys., November, 2023
Neural Networks, April, 2023
On the influence of over-parameterization in manifold based surrogates and deep neural operators.
J. Comput. Phys., April, 2023
Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons.
J. Comput. Phys., March, 2023
Neural operator prediction of linear instability waves in high-speed boundary layers.
J. Comput. Phys., February, 2023
J. Comput. Phys., 2023
Hutchinson Trace Estimation for High-Dimensional and High-Order Physics-Informed Neural Networks.
CoRR, 2023
AI-Lorenz: A physics-data-driven framework for black-box and gray-box identification of chaotic systems with symbolic regression.
CoRR, 2023
Rethinking materials simulations: Blending direct numerical simulations with neural operators.
CoRR, 2023
GPT vs Human for Scientific Reviews: A Dual Source Review on Applications of ChatGPT in Science.
CoRR, 2023
Bias-Variance Trade-off in Physics-Informed Neural Networks with Randomized Smoothing for High-Dimensional PDEs.
CoRR, 2023
Mechanical Characterization and Inverse Design of Stochastic Architected Metamaterials Using Neural Operators.
CoRR, 2023
Uncertainty quantification for noisy inputs-outputs in physics-informed neural networks and neural operators.
CoRR, 2023
Leveraging Hamilton-Jacobi PDEs with time-dependent Hamiltonians for continual scientific machine learning.
CoRR, 2023
Operator Learning Enhanced Physics-informed Neural Networks for Solving Partial Differential Equations Characterized by Sharp Solutions.
CoRR, 2023
CoRR, 2023
Learning characteristic parameters and dynamics of centrifugal pumps under multi-phase flow using physics-informed neural networks.
CoRR, 2023
DON-LSTM: Multi-Resolution Learning with DeepONets and Long Short-Term Memory Neural Networks.
CoRR, 2023
AI-Aristotle: A Physics-Informed framework for Systems Biology Gray-Box Identification.
CoRR, 2023
CoRR, 2023
Sound propagation in realistic interactive 3D scenes with parameterized sources using deep neural operators.
CoRR, 2023
CoRR, 2023
Characterization of partial wetting by CMAS droplets using multiphase many-body dissipative particle dynamics and data-driven discovery based on PINNs.
CoRR, 2023
Discovering a reaction-diffusion model for Alzheimer's disease by combining PINNs with symbolic regression.
CoRR, 2023
TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformers.
CoRR, 2023
CoRR, 2023
Enhancing training of physics-informed neural networks using domain-decomposition based preconditioning strategies.
CoRR, 2023
A novel deeponet model for learning moving-solution operators with applications to earthquake hypocenter localization.
CoRR, 2023
A Framework Based on Symbolic Regression Coupled with eXtended Physics-Informed Neural Networks for Gray-Box Learning of Equations of Motion from Data.
CoRR, 2023
A Generative Modeling Framework for Inferring Families of Biomechanical Constitutive Laws in Data-Sparse Regimes.
CoRR, 2023
Physics-informed neural networks for predicting gas flow dynamics and unknown parameters in diesel engines.
CoRR, 2023
Splitting physics-informed neural networks for inferring the dynamics of integer- and fractional-order neuron models.
CoRR, 2023
CoRR, 2023
Leveraging Multi-time Hamilton-Jacobi PDEs for Certain Scientific Machine Learning Problems.
CoRR, 2023
Bi-orthogonal fPINN: A physics-informed neural network method for solving time-dependent stochastic fractional PDEs.
CoRR, 2023
Neural Operator Learning for Long-Time Integration in Dynamical Systems with Recurrent Neural Networks.
CoRR, 2023
Deep neural operators can serve as accurate surrogates for shape optimization: A case study for airfoils.
CoRR, 2023
2022
Deep transfer operator learning for partial differential equations under conditional shift.
Nat. Mac. Intell., December, 2022
PLoS Comput. Biol., October, 2022
Potential Flow Generator With L<sub>2</sub> Optimal Transport Regularity for Generative Models.
IEEE Trans. Neural Networks Learn. Syst., 2022
A Physics-Informed Neural Network for Quantifying the Microstructural Properties of Polycrystalline Nickel Using Ultrasound Data: A promising approach for solving inverse problems.
IEEE Signal Process. Mag., 2022
Generative Ensemble Regression: Learning Particle Dynamics from Observations of Ensembles with Physics-informed Deep Generative Models.
SIAM J. Sci. Comput., 2022
SympOCnet: Solving Optimal Control Problems with Applications to High-Dimensional Multiagent Path Planning Problems.
SIAM J. Sci. Comput., 2022
SIAM J. Sci. Comput., 2022
PLoS Comput. Biol., 2022
PLoS Comput. Biol., 2022
Approximation rates of DeepONets for learning operators arising from advection-diffusion equations.
Neural Networks, 2022
Nat. Comput. Sci., 2022
A spectral method for stochastic fractional PDEs using dynamically-orthogonal/bi-orthogonal decomposition.
J. Comput. Phys., 2022
J. Comput. Phys., 2022
J. Comput. Phys., 2022
Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions.
Neurocomputing, 2022
Reliable extrapolation of deep neural operators informed by physics or sparse observations.
CoRR, 2022
SMS: Spiking Marching Scheme for Efficient Long Time Integration of Differential Equations.
CoRR, 2022
Augmented Physics-Informed Neural Networks (APINNs): A gating network-based soft domain decomposition methodology.
CoRR, 2022
On the Geometry Transferability of the Hybrid Iterative Numerical Solver for Differential Equations.
CoRR, 2022
How important are activation functions in regression and classification? A survey, performance comparison, and future directions.
CoRR, 2022
A Hybrid Iterative Numerical Transferable Solver (HINTS) for PDEs Based on Deep Operator Network and Relaxation Methods.
CoRR, 2022
Fractional SEIR Model and Data-Driven Predictions of COVID-19 Dynamics of Omicron Variant.
CoRR, 2022
Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems.
CoRR, 2022
Neural operator learning of heterogeneous mechanobiological insults contributing to aortic aneurysms.
CoRR, 2022
Deep transfer learning for partial differential equations under conditional shift with DeepONet.
CoRR, 2022
Learning two-phase microstructure evolution using neural operators and autoencoder architectures.
CoRR, 2022
CoRR, 2022
Interfacing Finite Elements with Deep Neural Operators for Fast Multiscale Modeling of Mechanics Problems.
CoRR, 2022
Deep learning of inverse water waves problems using multi-fidelity data: Application to Serre-Green-Naghdi equations.
CoRR, 2022
Systems Biology: Identifiability analysis and parameter identification via systems-biology informed neural networks.
CoRR, 2022
SympOCnet: Solving optimal control problems with applications to high-dimensional multi-agent path planning problems.
CoRR, 2022
2021
Solving Inverse Stochastic Problems from Discrete Particle Observations Using the Fokker-Planck Equation and Physics-Informed Neural Networks.
SIAM J. Sci. Comput., 2021
An integrated framework for building trustworthy data-driven epidemiological models: Application to the COVID-19 outbreak in New York City.
PLoS Comput. Biol., 2021
How the spleen reshapes and retains young and old red blood cells: A computational investigation.
PLoS Comput. Biol., 2021
Deep transfer learning and data augmentation improve glucose levels prediction in type 2 diabetes patients.
npj Digit. Medicine, 2021
Identifiability and predictability of integer- and fractional-order epidemiological models using physics-informed neural networks.
Nat. Comput. Sci., 2021
Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators.
Nat. Mach. Intell., 2021
Active- and transfer-learning applied to microscale-macroscale coupling to simulate viscoelastic flows.
J. Comput. Phys., 2021
B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data.
J. Comput. Phys., 2021
J. Comput. Phys., 2021
J. Comput. Phys., 2021
DeepM&Mnet for hypersonics: Predicting the coupled flow and finite-rate chemistry behind a normal shock using neural-network approximation of operators.
J. Comput. Phys., 2021
Physics-informed neural networks for solving forward and inverse flow problems via the Boltzmann-BGK formulation.
J. Comput. Phys., 2021
NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations.
J. Comput. Phys., 2021
DeepM&Mnet: Inferring the electroconvection multiphysics fields based on operator approximation by neural networks.
J. Comput. Phys., 2021
An open-source parallel code for computing the spectral fractional Laplacian on 3D complex geometry domains.
Comput. Phys. Commun., 2021
Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems.
CoRR, 2021
GFINNs: GENERIC Formalism Informed Neural Networks for Deterministic and Stochastic Dynamical Systems.
CoRR, 2021
Simulating progressive intramural damage leading to aortic dissection using an operator-regression neural network.
CoRR, 2021
A physics-informed variational DeepONet for predicting the crack path in brittle materials.
CoRR, 2021
AOSLO-net: A deep learning-based method for automatic segmentation of retinal microaneurysms from adaptive optics scanning laser ophthalmoscope images.
CoRR, 2021
Convergence rate of DeepONets for learning operators arising from advection-diffusion equations.
CoRR, 2021
CoRR, 2021
Measure-conditional Discriminator with Stationary Optimum for GANs and Statistical Distance Surrogates.
CoRR, 2021
CoRR, 2021
Extended Physics-informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition based Deep Learning Framework for Nonlinear Partial Differential Equations.
Proceedings of the AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, Stanford, CA, USA, March 22nd - to, 2021
2020
Learning in Modal Space: Solving Time-Dependent Stochastic PDEs Using Physics-Informed Neural Networks.
SIAM J. Sci. Comput., 2020
Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations.
SIAM J. Sci. Comput., 2020
A three-dimensional phase-field model for multiscale modeling of thrombus biomechanics in blood vessels.
PLoS Comput. Biol., 2020
PLoS Comput. Biol., 2020
SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems.
Neural Networks, 2020
Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness.
Neural Networks, 2020
J. Comput. Phys., 2020
A stabilized semi-implicit Fourier spectral method for nonlinear space-fractional reaction-diffusion equations.
J. Comput. Phys., 2020
nPINNs: Nonlocal physics-informed neural networks for a parametrized nonlocal universal Laplacian operator. Algorithms and applications.
J. Comput. Phys., 2020
A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems.
J. Comput. Phys., 2020
J. Comput. Phys., 2020
Adaptive activation functions accelerate convergence in deep and physics-informed neural networks.
J. Comput. Phys., 2020
CoRR, 2020
Physics-Informed Neural Networks for Nonhomogeneous Material Identification in Elasticity Imaging.
CoRR, 2020
Generative Ensemble-Regression: Learning Stochastic Dynamics from Discrete Particle Ensemble Observations.
CoRR, 2020
Convergence analysis of the time-stepping numerical methods for time-fractional nonlinear subdiffusion equations.
CoRR, 2020
Physics-informed neural network for ultrasound nondestructive quantification of surface breaking cracks.
CoRR, 2020
CoRR, 2020
CoRR, 2020
Symplectic networks: Intrinsic structure-preserving networks for identifying Hamiltonian systems.
CoRR, 2020
Nonlocal Physics-Informed Neural Networks - A Unified Theoretical and Computational Framework for Nonlocal Models.
Proceedings of the AAAI 2020 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, Stanford, CA, USA, March 23rd - to, 2020
2019
A Spectral Penalty Method for Two-Sided Fractional Differential Equations with General Boundary Conditions.
SIAM J. Sci. Comput., 2019
Efficient Multistep Methods for Tempered Fractional Calculus: Algorithms and Simulations.
SIAM J. Sci. Comput., 2019
SIAM J. Sci. Comput., 2019
Sci. Robotics, 2019
Integrating machine learning and multiscale modeling - perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences.
npj Digit. Medicine, 2019
Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems.
J. Comput. Phys., 2019
A stabilized phase-field method for two-phase flow at high Reynolds number and large density/viscosity ratio.
J. Comput. Phys., 2019
J. Comput. Phys., 2019
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations.
J. Comput. Phys., 2019
Neural-net-induced Gaussian process regression for function approximation and PDE solution.
J. Comput. Phys., 2019
Supervised parallel-in-time algorithm for long-time Lagrangian simulations of stochastic dynamics: Application to hydrodynamics.
J. Comput. Phys., 2019
Learning functionals via LSTM neural networks for predicting vessel dynamics in extreme sea states.
CoRR, 2019
Variational Physics-Informed Neural Networks For Solving Partial Differential Equations.
CoRR, 2019
CoRR, 2019
Learning and Meta-Learning of Stochastic Advection-Diffusion-Reaction Systems from Sparse Measurements.
CoRR, 2019
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators.
CoRR, 2019
Locally adaptive activation functions with slope recovery term for deep and physics-informed neural networks.
CoRR, 2019
Potential Flow Generator with $L_2$ Optimal Transport Regularity for Generative Models.
CoRR, 2019
Trainability and Data-dependent Initialization of Over-parameterized ReLU Neural Networks.
CoRR, 2019
Proceedings of the Third IEEE/ACM Workshop on Deep Learning on Supercomputers, 2019
2018
A New Class of Semi-Implicit Methods with Linear Complexity for Nonlinear Fractional Differential Equations.
SIAM J. Sci. Comput., 2018
A Computational Stochastic Methodology for the Design of Random Meta-materials under Geometric Constraints.
SIAM J. Sci. Comput., 2018
Numerical Gaussian Processes for Time-Dependent and Nonlinear Partial Differential Equations.
SIAM J. Sci. Comput., 2018
SIAM J. Numer. Anal., 2018
A Spectral Method (of Exponential Convergence) for Singular Solutions of the Diffusion Equation with General Two-Sided Fractional Derivative.
SIAM J. Numer. Anal., 2018
Active learning of constitutive relation from mesoscopic dynamics for macroscopic modeling of non-Newtonian flows.
J. Comput. Phys., 2018
Bi-directional coupling between a PDE-domain and an adjacent Data-domain equipped with multi-fidelity sensors.
J. Comput. Phys., 2018
J. Comput. Phys., 2018
J. Comput. Phys., 2018
Linking Gaussian Process regression with data-driven manifold embeddings for nonlinear data fusion.
CoRR, 2018
Hidden Fluid Mechanics: A Navier-Stokes Informed Deep Learning Framework for Assimilating Flow Visualization Data.
CoRR, 2018
2017
A Generalized Spectral Collocation Method with Tunable Accuracy for Fractional Differential Equations with End-Point Singularities.
SIAM J. Sci. Comput., 2017
Computing Fractional Laplacians on Complex-Geometry Domains: Algorithms and Simulations.
SIAM J. Sci. Comput., 2017
A Petrov-Galerkin Spectral Method of Linear Complexity for Fractional Multiterm ODEs on the Half Line.
SIAM J. Sci. Comput., 2017
Petrov-Galerkin and Spectral Collocation Methods for Distributed Order Differential Equations.
SIAM J. Sci. Comput., 2017
A deep convolutional neural network for classification of red blood cells in sickle cell anemia.
PLoS Comput. Biol., 2017
Patient-specific modeling of individual sickle cell behavior under transient hypoxia.
PLoS Comput. Biol., 2017
J. Comput. Phys., 2017
J. Comput. Phys., 2017
J. Comput. Phys., 2017
Discovering variable fractional orders of advection-dispersion equations from field data using multi-fidelity Bayesian optimization.
J. Comput. Phys., 2017
Fractional Burgers equation with nonlinear non-locality: Spectral vanishing viscosity and local discontinuous Galerkin methods.
J. Comput. Phys., 2017
J. Comput. Phys., 2017
A general CFD framework for fault-resilient simulations based on multi-resolution information fusion.
J. Comput. Phys., 2017
A resilient and efficient CFD framework: Statistical learning tools for multi-fidelity and heterogeneous information fusion.
J. Comput. Phys., 2017
J. Comput. Phys., 2017
A robust bi-orthogonal/dynamically-orthogonal method using the covariance pseudo-inverse with application to stochastic flow problems.
J. Comput. Phys., 2017
GPU-accelerated red blood cells simulations with transport dissipative particle dynamics.
Comput. Phys. Commun., 2017
Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations.
CoRR, 2017
Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations.
CoRR, 2017
CoRR, 2017
Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations.
CoRR, 2017
CoRR, 2017
Efficient two-dimensional simulations of the fractional Szabo equation with different time-stepping schemes.
Comput. Math. Appl., 2017
2016
Multifidelity Information Fusion Algorithms for High-Dimensional Systems and Massive Data sets.
SIAM J. Sci. Comput., 2016
Implicit-Explicit Difference Schemes for Nonlinear Fractional Differential Equations with Nonsmooth Solutions.
SIAM J. Sci. Comput., 2016
MD/DPD Multiscale Framework for Predicting Morphology and Stresses of Red Blood Cells in Health and Disease.
PLoS Comput. Biol., 2016
Visualizing multiphysics, fluid-structure interaction phenomena in intracranial aneurysms.
Parallel Comput., 2016
Strong and weak convergence order of finite element methods for stochastic PDEs with spatial white noise.
Numerische Mathematik, 2016
Fast difference schemes for solving high-dimensional time-fractional subdiffusion equations.
J. Comput. Phys., 2016
J. Comput. Phys., 2016
Flow in complex domains simulated by Dissipative Particle Dynamics driven by geometry-specific body-forces.
J. Comput. Phys., 2016
J. Comput. Phys., 2016
2015
Enabling High-Dimensional Hierarchical Uncertainty Quantification by ANOVA and Tensor-Train Decomposition.
IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2015
Adaptive Wick-Malliavin Approximation to Nonlinear SPDEs with Discrete Random Variables.
SIAM J. Sci. Comput., 2015
SIAM J. Sci. Comput., 2015
A Generalized Spectral Collocation Method with Tunable Accuracy for Variable-Order Fractional Differential Equations.
SIAM J. Sci. Comput., 2015
SIAM J. Sci. Comput., 2015
SIAM J. Sci. Comput., 2015
Numerical Methods for Stochastic Delay Differential Equations Via the Wong-Zakai Approximation.
SIAM J. Sci. Comput., 2015
Optimal Error Estimates of Spectral Petrov-Galerkin and Collocation Methods for Initial Value Problems of Fractional Differential Equations.
SIAM J. Numer. Anal., 2015
Wiener Chaos Versus Stochastic Collocation Methods for Linear Advection-Diffusion-Reaction Equations with Multiplicative White Noise.
SIAM J. Numer. Anal., 2015
Inflow/Outflow Boundary Conditions for Particle-Based Blood Flow Simulations: Application to Arterial Bifurcations and Trees.
PLoS Comput. Biol., 2015
Second-order approximations for variable order fractional derivatives: Algorithms and applications.
J. Comput. Phys., 2015
Fractional spectral collocation methods for linear and nonlinear variable order FPDEs.
J. Comput. Phys., 2015
Multiscale Universal Interface: A concurrent framework for coupling heterogeneous solvers.
J. Comput. Phys., 2015
Quantification of sampling uncertainty for molecular dynamics simulation: Time-dependent diffusion coefficient in simple fluids.
J. Comput. Phys., 2015
J. Comput. Phys., 2015
Multi-resolution flow simulations by smoothed particle hydrodynamics via domain decomposition.
J. Comput. Phys., 2015
CoRR, 2015
The in-silico lab-on-a-chip: petascale and high-throughput simulations of microfluidics at cell resolution.
Proceedings of the International Conference for High Performance Computing, 2015
2014
A Recursive Sparse Grid Collocation Method for Differential Equations with White Noise.
SIAM J. Sci. Comput., 2014
Discontinuous Spectral Element Methods for Time- and Space-Fractional Advection Equations.
SIAM J. Sci. Comput., 2014
SIAM J. Sci. Comput., 2014
Time Correlation Functions of Brownian Motion and Evaluation of Friction Coefficient in the Near-Brownian-Limit Regime.
Multiscale Model. Simul., 2014
Multiscale Model. Simul., 2014
J. Comput. Phys., 2014
Energy-conserving dissipative particle dynamics with temperature-dependent properties.
J. Comput. Phys., 2014
A robust and accurate outflow boundary condition for incompressible flow simulations on severely-truncated unbounded domains.
J. Comput. Phys., 2014
On the equivalence of dynamically orthogonal and bi-orthogonal methods: Theory and numerical simulations.
J. Comput. Phys., 2014
Accelerating dissipative particle dynamics simulations on GPUs: Algorithms, numerics and applications.
Comput. Phys. Commun., 2014
Stochastic testing simulator for integrated circuits and MEMS: Hierarchical and sparse techniques.
Proceedings of the IEEE 2014 Custom Integrated Circuits Conference, 2014
2013
SIAM J. Sci. Comput., 2013
Numerical solution of the Stratonovich- and Ito-Euler equations: Application to the stochastic piston problem.
J. Comput. Phys., 2013
J. Comput. Phys., 2013
Generalized fictitious methods for fluid-structure interactions: Analysis and simulations.
J. Comput. Phys., 2013
Reweighted <i>ℓ</i><sub>1</sub>ℓ1 minimization method for stochastic elliptic differential equations.
J. Comput. Phys., 2013
J. Comput. Phys., 2013
A convergence study for SPDEs using combined Polynomial Chaos and Dynamically-Orthogonal schemes.
J. Comput. Phys., 2013
2012
A Multistage Wiener Chaos Expansion Method for Stochastic Advection-Diffusion-Reaction Equations.
SIAM J. Sci. Comput., 2012
Error Estimates for the ANOVA Method with Polynomial Chaos Interpolation: Tensor Product Functions.
SIAM J. Sci. Comput., 2012
A hybrid spectral/DG method for solving the phase-averaged ocean wave equation: Algorithm and validation.
J. Comput. Phys., 2012
J. Comput. Phys., 2012
New evolution equations for the joint response-excitation probability density function of stochastic solutions to first-order nonlinear PDEs.
J. Comput. Phys., 2012
A convergence study of a new partitioned fluid-structure interaction algorithm based on fictitious mass and damping.
J. Comput. Phys., 2012
Tightly Coupled Atomistic-Continuum Simulations of Brain Blood Flow on Petaflop Supercomputers.
Comput. Sci. Eng., 2012
Proceedings of the 1st Conference of the Extreme Science and Engineering Discovery Environment, 2012
2011
PLoS Comput. Biol., 2011
J. Comput. Phys., 2011
Sub-iteration leads to accuracy and stability enhancements of semi-implicit schemes for the Navier-Stokes equations.
J. Comput. Phys., 2011
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis, 2011
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis, 2011
A new computational paradigm in multiscale simulations: application to brain blood flow.
Proceedings of the Conference on High Performance Computing Networking, 2011
2010
Uncertainty quantification in simulations of power systems: Multi-element polynomial chaos methods.
Reliab. Eng. Syst. Saf., 2010
A new domain decomposition method with overlapping patches for ultrascale simulations: Application to biological flows.
J. Comput. Phys., 2010
J. Comput. Phys., 2010
2009
Parallel performance of the coarse space linear vertex solver and low energy basis preconditioner for spectral/hp elements.
Parallel Comput., 2009
J. Comput. Phys., 2009
J. Comput. Phys., 2009
2008
The multi-element probabilistic collocation method (ME-PCM): Error analysis and applications.
J. Comput. Phys., 2008
2007
IEEE Trans. Vis. Comput. Graph., 2007
IEEE Trans. Medical Imaging, 2007
Towards stable coupling methods for high-order discretization of fluid-structure interaction: Algorithms and observations.
J. Comput. Phys., 2007
NEKTAR, SPICE and Vortonics: using federated grids for large scale scientific applications.
Clust. Comput., 2007
2006
SIAM J. Sci. Comput., 2006
A family of time-staggered schemes for integrating hybrid DPD models for polymers: Algorithms and applications.
J. Comput. Phys., 2006
J. Comput. Phys., 2006
Future Gener. Comput. Syst., 2006
Proceedings of the ACM/IEEE SC2006 Conference on High Performance Networking and Computing, 2006
Proceedings of the 20th International Parallel and Distributed Processing Symposium (IPDPS 2006), 2006
Proceedings of the Computational Science, 2006
2005
J. Sci. Comput., 2005
Selecting the Numerical Flux in Discontinuous Galerkin Methods for Diffusion Problems.
J. Sci. Comput., 2005
Proceedings of the Computational Science, 2005
2004
SIAM J. Sci. Comput., 2004
SIAM J. Sci. Comput., 2004
IEEE Computer Graphics and Applications, 2004
Proceedings of the 15th IEEE Visualization Conference, 2004
Proceedings of the International Conference on Computer Graphics and Interactive Techniques, 2004
Proceedings of the International Conference on Computer Graphics and Interactive Techniques, 2004
Proceedings of the Computational Science, 2004
2003
Proceedings of the Computational Science - ICCS 2003, 2003
Parallel Scientific Computing in C++ and MPI - A Seamless Approach to Parallel Algorithms and their Implementation.
Cambridge University Press, ISBN: 978-0-521-52080-5, 2003
2002
SIAM J. Sci. Comput., 2002
A Semi-Lagrangian Method for Turbulence Simulations Using Mixed Spectral Discretizations.
J. Sci. Comput., 2002
J. Sci. Comput., 2002
Proceedings of the 2002 ACM/IEEE conference on Supercomputing, 2002
2000
Proceedings of the 11th IEEE Visualization Conference, 2000
1999
SIAM J. Sci. Comput., 1999
Proceedings of the ACM/IEEE Conference on Supercomputing, 1999
1997
Unsteady Two-Dimensional Flows in Complex Geometries: Comparative Bifurcation Studies with Global Eigenfunction Expansions.
SIAM J. Sci. Comput., 1997
hpDevelopment of a Parallel Unstructured Spectral/ Method for Unsteady Fluid Dynamics.
Proceedings of the Conference on Parallel Computational Fluid Dynamics 1997, 1997
1996
Communication Performance Models in Prism : A Spectral Element-Fourier Parallel Navier-Stokes Solver.
Proceedings of the 1996 ACM/IEEE Conference on Supercomputing, 1996