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ORCIDAkil Narayan 0001
Akil C. Narayan
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- affiliation: University of Utah, Salt Lake City, UT, USA
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2020 – today
- 2024
[j64]Nuojin Cheng, Osman Asif Malik
, Yiming Xu, Stephen Becker, Alireza Doostan, Akil Narayan:
Subsampling of Parametric Models with Bifidelity Boosting. SIAM/ASA J. Uncertain. Quantification 12(2): 213-241 (2024)- [j63]Ruijian Han, Boris Kramer, Dongjin Lee, Akil Narayan, Yiming Xu:
An Approximate Control Variates Approach to Multifidelity Distribution Estimation. SIAM/ASA J. Uncertain. Quantification 12(4): 1349-1388 (2024) - [c20]Shibo Li, Xin Yu, Wei W. Xing, Robert M. Kirby, Akil Narayan, Shandian Zhe:
Multi-Resolution Active Learning of Fourier Neural Operators. AISTATS 2024: 2440-2448 - [i43]Ankur, Ram Jiwari, Akil Narayan:
Conformal Finite Element Methods for Nonlinear Rosenau-Burgers-Biharmonic Models. CoRR abs/2402.08926 (2024) - [i42]Yanlai Chen, Yajie Ji, Akil Narayan, Zhenli Xu:
TGPT-PINN: Nonlinear model reduction with transformed GPT-PINNs. CoRR abs/2403.03459 (2024) - [i41]Matthew Lowery, John Turnage, Zachary Morrow, John D. Jakeman, Akil Narayan, Shandian Zhe, Varun Shankar:
Kernel Neural Operators (KNOs) for Scalable, Memory-efficient, Geometrically-flexible Operator Learning. CoRR abs/2407.00809 (2024) - [i40]Da Long, Zhitong Xu, Guang Yang, Akil Narayan, Shandian Zhe:
Arbitrarily-Conditioned Multi-Functional Diffusion for Multi-Physics Emulation. CoRR abs/2410.13794 (2024) - [i39]Yekaterina Epshteyn, Akil Narayan, Yinqian Yu:
Energy Stable and Structure-Preserving Algorithms for the Stochastic Galerkin System of 2D Shallow Water Equations. CoRR abs/2412.16353 (2024) - 2023
- [j62]Akil Narayan, Zexin Liu, Jake A. Bergquist, Chantel Charlebois, Sumientra Rampersad, Lindsay C. Rupp, Dana H. Brooks, Dan White, Jess D. Tate, Rob S. MacLeod:
UncertainSCI: Uncertainty quantification for computational models in biomedicine and bioengineering. Comput. Biol. Medicine 152: 106407 (2023) - [j61]Yiming Xu, Akil Narayan:
Randomized weakly admissible meshes. J. Approx. Theory 285: 105835 (2023) - [j60]Michael Penwarden, Shandian Zhe, Akil Narayan, Robert M. Kirby:
A metalearning approach for Physics-Informed Neural Networks (PINNs): Application to parameterized PDEs. J. Comput. Phys. 477: 111912 (2023) - [j59]Vidhi Zala, Akil Narayan, Robert M. Kirby:
Convex optimization-based structure-preserving filter for multidimensional finite element simulations. J. Comput. Phys. 492: 112364 (2023) - [j58]Jess D. Tate, Zexin Liu, Jake A. Bergquist, Sumientra Rampersad, Dan White, Chantel Charlebois, Lindsay C. Rupp, Dana H. Brooks, Rob S. MacLeod, Akil Narayan:
UncertainSCI: A Python Package for Noninvasive Parametric Uncertainty Quantification of Simulation Pipelines. J. Open Source Softw. 8(90): 4249 (2023) - [j57]Justin M. Baker, Elena Cherkaev, Akil Narayan, Bao Wang:
Learning Proper Orthogonal Decomposition of Complex Dynamics Using Heavy-ball Neural ODEs. J. Sci. Comput. 95(2): 54 (2023) - [j56]Yiming Xu, Akil Narayan:
Budget-limited distribution learning in multifidelity problems. Numerische Mathematik 153(1): 171-212 (2023) - [j55]Zexin Liu, Akil Narayan:
A Stieltjes Algorithm for Generating Multivariate Orthogonal Polynomials. SIAM J. Sci. Comput. 45(3): 1125-1147 (2023) - [c19]Shibo Li, Zheng Wang, Akil Narayan, Robert M. Kirby, Shandian Zhe:
Meta-Learning with Adjoint Methods. AISTATS 2023: 7239-7251 - [c18]Jake A. Bergquist, Matthias Lange, Brian Zenger, Benjamin A. Orkild, Eric Paccione, Eugene Kwan, Bram Hunt, Jiawei Dong, Rob S. MacLeod, Akil Narayan, Ravi Ranjan:
Uncertainty Quantification of the Effect of Variable Conductivity in Ventricular Fibrotic Regions on Ventricular Tachycardia. CinC 2023: 1-4 - [c17]Anna Busatto, Lindsay C. Rupp, Karli Gillette, Akil Narayan, Gernot Plank, Rob S. MacLeod:
Capturing the Influence of Conduction Velocity on Epicardial Activation Patterns Using Uncertainty Quantification. CinC 2023: 1-4 - [c16]Benjamin A. Orkild, Jake A. Bergquist, Eric Paccione, Matthias Lange, Eugene Kwan, Bram Hunt, Rob S. MacLeod, Akil Narayan, Ravi Ranjan:
A Grid Search of Fibrosis Thresholds for Uncertainty Quantification in Atrial Flutter Simulations. CinC 2023: 1-4 - [c15]Lindsay C. Rupp, Anna Busatto, Jake A. Bergquist, Karli Gillette, Akil Narayan, Gernot Plank, Rob S. MacLeod:
Uncertainty Quantification of Fiber Orientation and Epicardial Activation. CinC 2023: 1-4 - [c14]Shibo Li, Michael Penwarden, Yiming Xu, Conor Tillinghast, Akil Narayan, Mike Kirby, Shandian Zhe:
Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks. ICML 2023: 19855-19881 - [i38]Ruijian Han, Akil Narayan, Yiming Xu:
An approximate control variates approach to multifidelity distribution estimation. CoRR abs/2303.06422 (2023) - [i37]Shibo Li, Xin Yu, Wei W. Xing, Mike Kirby, Akil Narayan, Shandian Zhe:
Multi-Resolution Active Learning of Fourier Neural Operators. CoRR abs/2309.16971 (2023) - [i36]Dihan Dai, Yekaterina Epshteyn, Akil Narayan:
Energy Stable and Structure-Preserving Schemes for the Stochastic Galerkin Shallow Water Equations. CoRR abs/2310.06229 (2023) - 2022
- [j54]Ryleigh A. Moore, Akil Narayan:
Adaptive density tracking by quadrature for stochastic differential equations. Appl. Math. Comput. 431: 127298 (2022) - [j53]Michael Penwarden, Shandian Zhe, Akil Narayan, Robert M. Kirby:
Multifidelity modeling for Physics-Informed Neural Networks (PINNs). J. Comput. Phys. 451: 110844 (2022) - [j52]Dihan Dai, Yekaterina Epshteyn, Akil Narayan:
Hyperbolicity-preserving and well-balanced stochastic Galerkin method for two-dimensional shallow water equations. J. Comput. Phys. 452: 110901 (2022) - [j51]Edward Laughton, Vidhi Zala, Akil Narayan, Robert M. Kirby, David Moxey:
Fast Barycentric-Based Evaluation Over Spectral/hp Elements. J. Sci. Comput. 90(2): 78 (2022) - [j50]Elizabeth Qian, Jemima M. Tabeart, Christopher Beattie, Serkan Gugercin, Jiahua Jiang, Peter R. Kramer, Akil Narayan:
Model Reduction of Linear Dynamical Systems via Balancing for Bayesian Inference. J. Sci. Comput. 91(1): 29 (2022) - [j49]Yiming Xu, Vahid Keshavarzzadeh, Robert M. Kirby, Akil Narayan:
A Bandit-Learning Approach to Multifidelity Approximation. SIAM J. Sci. Comput. 44(1): 150- (2022) - [c13]Jake A. Bergquist, Lindsay C. Rupp, Anna Busatto, Ben Orkild, Brian Zenger, Wilson Good, Jaume Coll-Font, Akil Narayan, Jess D. Tate, Dana H. Brooks, Rob S. MacLeod:
Heart Position Uncertainty Quantification in the Inverse Problem of ECGI. CinC 2022: 1-4 - [c12]Narimane Gassa, Machteld J. Boonstra, Beata Ondrusova, Jana Svehlíková, Dana H. Brooks, Akil Narayan, Ali S. Rababah, Peter M. van Dam, Rob S. MacLeod, Jess D. Tate, Nejib Zemzemi:
Effect of Segmentation Uncertainty on the ECGI Inverse Problem Solution and Source Localization. CinC 2022: 1-4 - [c11]Xiajun Jiang, Jess D. Tate, Jake Bergquist, Akil Narayan, Rob S. MacLeod, Linwei Wang:
Uncertainty Quantification of Cardiac Position on Deep Graph Network ECGI. CinC 2022: 1-4 - [c10]Beata Ondrusova, Machteld J. Boonstra, Jana Svehlíková, Dana H. Brooks, Peter M. van Dam, Ali S. Rababah, Akil Narayan, Rob S. MacLeod, Nejib Zemzemi, Jess D. Tate:
The Effect of Segmentation Variability in Forward ECG Simulation. CinC 2022: 1-4 - [c9]Jess D. Tate, Nejib Zemzemi, Shireen Y. Elhabian, Beáta Ondrusová, Machteld J. Boonstra, Peter M. van Dam, Akil Narayan, Dana H. Brooks, Rob S. MacLeod:
Segmentation Uncertainty Quantification in Cardiac Propagation Models. CinC 2022: 1-4 - [c8]Shikai Fang, Akil Narayan, Robert M. Kirby, Shandian Zhe:
Bayesian Continuous-Time Tucker Decomposition. ICML 2022: 6235-6245 - [c7]Zheng Wang, Yiming Xu, Conor Tillinghast, Shibo Li, Akil Narayan, Shandian Zhe:
Nonparametric Embeddings of Sparse High-Order Interaction Events. ICML 2022: 23237-23253 - [i35]Vahid Keshavarzzadeh, Shandian Zhe, Robert M. Kirby, Akil Narayan:
GP-HMAT: Scalable, O(n log(n)) Gaussian Process Regression with Hierarchical Low-Rank Matrices. CoRR abs/2201.00888 (2022) - [i34]Zexin Liu, Akil Narayan:
A Stieltjes algorithm for generating multivariate orthogonal polynomials. CoRR abs/2202.04843 (2022) - [i33]Justin M. Baker, Elena Cherkaev, Akil Narayan, Bao Wang:
Learning POD of Complex Dynamics Using Heavy-ball Neural ODEs. CoRR abs/2202.12373 (2022) - [i32]Vidhi Zala, Akil Narayan, Robert M. Kirby:
Convex Optimization-Based Structure-Preserving Filter For Multidimensional Finite Element Simulations. CoRR abs/2203.09748 (2022) - [i31]Jarom D. Hogue, Robert M. Kirby, Akil Narayan:
Dimensionality Reduction in Deep Learning via Kronecker Multi-layer Architectures. CoRR abs/2204.04273 (2022) - [i30]Justin M. Baker, Hedi Xia, Yiwei Wang, Elena Cherkaev, Akil Narayan, Long Chen, Jack Xin, Andrea L. Bertozzi, Stanley J. Osher, Bao Wang:
Proximal Implicit ODE Solvers for Accelerating Learning Neural ODEs. CoRR abs/2204.08621 (2022) - [i29]Zheng Wang, Yiming Xu, Conor Tillinghast, Shibo Li, Akil Narayan, Shandian Zhe:
Nonparametric Embeddings of Sparse High-Order Interaction Events. CoRR abs/2207.03639 (2022) - [i28]Osman Asif Malik, Yiming Xu, Nuojin Cheng, Stephen Becker, Alireza Doostan, Akil Narayan:
Fast Algorithms for Monotone Lower Subsets of Kronecker Least Squares Problems. CoRR abs/2209.05662 (2022) - [i27]Nuojin Cheng, Osman Asif Malik, Yiming Xu, Stephen Becker, Alireza Doostan, Akil Narayan:
Quadrature Sampling of Parametric Models with Bi-fidelity Boosting. CoRR abs/2209.05705 (2022) - 2021
- [j48]Roland Pulch, Akil Narayan:
Sensitivity analysis of random linear dynamical systems using quadratic outputs. J. Comput. Appl. Math. 387: 112491 (2021) - [j47]Roland Pulch, Akil Narayan, Tatjana Stykel:
Sensitivity analysis of random linear differential-algebraic equations using system norms. J. Comput. Appl. Math. 397: 113666 (2021) - [j46]Akil Narayan, Liang Yan, Tao Zhou:
Optimal design for kernel interpolation: Applications to uncertainty quantification. J. Comput. Phys. 430: 110094 (2021) - [j45]Yanlai Chen, Lijie Ji, Akil Narayan, Zhenli Xu:
L1-Based Reduced Over Collocation and Hyper Reduction for Steady State and Time-Dependent Nonlinear Equations. J. Sci. Comput. 87(1): 10 (2021) - [j44]Zexin Liu, Akil Narayan:
On the Computation of Recurrence Coefficients for Univariate Orthogonal Polynomials. J. Sci. Comput. 88(3): 53 (2021) - [j43]Vahid Keshavarzzadeh, Robert M. Kirby, Akil Narayan:
Multilevel Designed Quadrature for Partial Differential Equations with Random Inputs. SIAM J. Sci. Comput. 43(2): A1412-A1440 (2021) - [j42]Dihan Dai, Yekaterina Epshteyn, Akil Narayan:
Hyperbolicity-Preserving and Well-Balanced Stochastic Galerkin Method for Shallow Water Equations. SIAM J. Sci. Comput. 43(2): A929-A952 (2021) - [j41]Vidhi Zala, Robert M. Kirby, Akil Narayan:
Structure-Preserving Nonlinear Filtering for Continuous and Discontinuous Galerkin Spectral/hp Element Methods. SIAM J. Sci. Comput. 43(6): A3713-A3732 (2021) - [c6]Jake A. Bergquist, Brian Zenger, Lindsay C. Rupp, Akil Narayan, Jess D. Tate, Rob S. MacLeod:
Uncertainty Quantification in Simulations of Myocardial Ischemia. CinC 2021: 1-4 - [c5]Lindsay C. Rupp, Jake A. Bergquist, Brian Zenger, Karli Gillette, Akil Narayan, Jess D. Tate, Gernot Plank, Rob S. MacLeod:
The Role of Myocardial Fiber Direction in Epicardial Activation Patterns via Uncertainty Quantification. CinC 2021: 1-4 - [c4]Jess D. Tate, Wilson W. Good, Nejib Zemzemi, Machteld J. Boonstra, Peter M. van Dam, Dana H. Brooks, Akil Narayan, Rob S. MacLeod:
Uncertainty Quantification of the Effects of Segmentation Variability in ECGI. FIMH 2021: 515-522 - [i26]Mani Razi, Robert M. Kirby, Akil Narayan:
Kernel optimization for Low-Rank Multi-Fidelity Algorithms. CoRR abs/2101.01769 (2021) - [i25]Yiming Xu, Akil Narayan:
Randomized weakly admissible meshes. CoRR abs/2101.04043 (2021) - [i24]Zexin Liu, Akil Narayan:
On the computation of recurrence coefficients for univariate orthogonal polynomials. CoRR abs/2101.11963 (2021) - [i23]Edward Laughton, Vidhi Zala, Akil Narayan, Robert M. Kirby, David Moxey:
Fast Barycentric-Based Evaluation Over Spectral/hp Elements. CoRR abs/2103.03594 (2021) - [i22]Yiming Xu, Vahid Keshavarzzadeh, Robert M. Kirby, Akil Narayan:
A bandit-learning approach to multifidelity approximation. CoRR abs/2103.15342 (2021) - [i21]Akil Narayan, Liang Yan, Tao Zhou:
Optimal design for kernel interpolation: applications to uncertainty quantification. CoRR abs/2104.06291 (2021) - [i20]Dihan Dai, Yekaterina Epshteyn, Akil Narayan:
Hyperbolicity-Preserving and Well-Balanced Stochastic Galerkin Method for Two-Dimensional Shallow Water Equations. CoRR abs/2104.11268 (2021) - [i19]Yiming Xu, Akil Narayan:
Budget-limited distribution learning in multifidelity problems. CoRR abs/2105.04599 (2021) - [i18]Ryleigh A. Moore, Akil Narayan:
Adaptive Density Tracking by Quadrature for Stochastic Differential Equations. CoRR abs/2105.08148 (2021) - [i17]Vidhi Zala, Robert M. Kirby, Akil Narayan:
Structure-preserving Nonlinear Filtering for Continuous and Discontinuous Galerkin Spectral/hp Element Methods. CoRR abs/2106.08316 (2021) - [i16]Michael Penwarden, Shandian Zhe, Akil Narayan, Robert M. Kirby:
Multifidelity Modeling for Physics-Informed Neural Networks (PINNs). CoRR abs/2106.13361 (2021) - [i15]Dihan Dai, Yekaterina Epshteyn, Akil Narayan:
Non-Dissipative and Structure-Preserving Emulators via Spherical Optimization. CoRR abs/2108.12053 (2021) - [i14]Shibo Li, Zheng Wang, Akil Narayan, Robert Michael Kirby, Shandian Zhe:
Meta-Learning with Adjoint Methods. CoRR abs/2110.08432 (2021) - [i13]Michael Penwarden, Shandian Zhe, Akil Narayan, Robert M. Kirby:
Physics-Informed Neural Networks (PINNs) for Parameterized PDEs: A Metalearning Approach. CoRR abs/2110.13361 (2021) - [i12]Elizabeth Qian, Jemima M. Tabeart, Christopher Beattie, Serkan Gugercin, Jiahua Jiang, Peter R. Kramer, Akil Narayan:
Model Reduction of Linear Dynamical Systems via Balancing for Bayesian Inference. CoRR abs/2111.13246 (2021) - 2020
- [j40]Vahid Keshavarzzadeh, Robert M. Kirby, Akil Narayan:
Generation of nested quadrature rules for generic weight functions via numerical optimization: Application to sparse grids. J. Comput. Phys. 400 (2020) - [j39]Ling Guo, Akil Narayan, Tao Zhou:
Constructing Least-Squares Polynomial Approximations. SIAM Rev. 62(2): 483-508 (2020) - [j38]Varun Shankar, Grady B. Wright, Akil Narayan:
A Robust Hyperviscosity Formulation for Stable RBF-FD Discretizations of Advection-Diffusion-Reaction Equations on Manifolds. SIAM J. Sci. Comput. 42(4): A2371-A2401 (2020) - [j37]Vidhi Zala, Mike Kirby, Akil Narayan:
Structure-Preserving Function Approximation via Convex Optimization. SIAM J. Sci. Comput. 42(5): A3006-A3029 (2020) - [j36]Roohallah Khatami, Masood Parvania, Akil Narayan:
Flexibility Reserve in Power Systems: Definition and Stochastic Multi-Fidelity Optimization. IEEE Trans. Smart Grid 11(1): 644-654 (2020) - [c3]Lindsay C. Rupp, Zexin Liu, Jake A. Bergquist, Sumientra Rampersad, Dan White, Jess D. Tate, Dana H. Brooks, Akil Narayan, Rob S. MacLeod:
Using UncertainSCI to Quantify Uncertainty in Cardiac Simulations. CinC 2020: 1-4 - [i11]Yiming Xu, Akil Narayan, Hoang Tran, Clayton G. Webster:
Analysis of The Ratio of $\ell_1$ and $\ell_2$ Norms in Compressed Sensing. CoRR abs/2004.05873 (2020) - [i10]Kyle M. Burk, Akil Narayan, Joseph A. Orr:
Efficient sampling for polynomial chaos-based uncertainty quantification and sensitivity analysis using weighted approximate Fekete points. CoRR abs/2008.04854 (2020) - [i9]Dihan Dai, Yekaterina Epshteyn, Akil Narayan:
Hyperbolicity-Preserving and Well-Balanced Stochastic Galerkin Method for Shallow Water Equations. CoRR abs/2008.08154 (2020) - [i8]Vidhi Zala, Robert M. Kirby, Akil Narayan:
Structure-preserving function approximation via convex optimization. CoRR abs/2008.08223 (2020) - [i7]Ling Guo, Akil Narayan, Yongle Liu, Tao Zhou:
Sparse approximation of data-driven Polynomial Chaos expansions: an induced sampling approach. CoRR abs/2008.10121 (2020) - [i6]Yanlai Chen, Lijie Ji, Akil Narayan, Zhenli Xu:
L1-based reduced over collocation and hyper reduction for steady state and time-dependent nonlinear equations. CoRR abs/2009.04812 (2020)
2010 – 2019
- 2019
- [j35]Yanlai Chen, Jiahua Jiang, Akil Narayan:
A robust error estimator and a residual-free error indicator for reduced basis methods. Comput. Math. Appl. 77(7): 1963-1979 (2019) - [j34]Mani Razi, Robert M. Kirby, Akil Narayan:
Fast predictive multi-fidelity prediction with models of quantized fidelity levels. J. Comput. Phys. 376: 992-1008 (2019) - [j33]Ming Cheng, Akil Narayan, Yi Qin, Peng Wang, Xinghui Zhong, Xueyu Zhu:
An efficient solver for cumulative density function-based solutions of uncertain kinematic wave models. J. Comput. Phys. 382: 138-151 (2019) - [j32]Lun Yang, Yi Qin, Akil Narayan, Peng Wang:
Data assimilation for models with parametric uncertainty. J. Comput. Phys. 396: 785-798 (2019) - [j31]Daniel J. Perry, Robert M. Kirby, Akil Narayan, Ross T. Whitaker:
Allocation Strategies for High Fidelity Models in the Multifidelity Regime. SIAM/ASA J. Uncertain. Quantification 7(1): 203-231 (2019) - [j30]Vahid Keshavarzzadeh, Robert M. Kirby, Akil Narayan:
Convergence Acceleration for Time-Dependent Parametric Multifidelity Models. SIAM J. Numer. Anal. 57(3): 1344-1368 (2019) - [j29]Roland Pulch, Akil Narayan:
Balanced Truncation for Model Order Reduction of Linear Dynamical Systems with Quadratic Outputs. SIAM J. Sci. Comput. 41(4): A2270-A2295 (2019) - [j28]Harbir Antil, Yanlai Chen, Akil Narayan:
Reduced Basis Methods for Fractional Laplace Equations via Extension. SIAM J. Sci. Comput. 41(6): A3552-A3575 (2019) - [c2]Barbara M. Johnston, Akil Narayan, Peter R. Johnston:
A Comparison of Methods for Examining the Effect of Uncertainty in the Conductivities in a Model of Partial Thickness Ischaemia. CinC 2019: 1-4 - [i5]Varun Shankar, Grady B. Wright, Akil Narayan:
A Robust Hyperviscosity Formulation for Stable RBF-FD Discretizations of Advection-Diffusion-Reaction Equations on Manifolds. CoRR abs/1910.07059 (2019) - 2018
- [j27]Ling Guo, Akil Narayan, Tao Zhou:
A gradient enhanced ℓ1-minimization for sparse approximation of polynomial chaos expansions. J. Comput. Phys. 367: 49-64 (2018) - [j26]Jerrad Hampton, Hillary R. Fairbanks, Akil Narayan, Alireza Doostan:
Practical error bounds for a non-intrusive bi-fidelity approach to parametric/stochastic model reduction. J. Comput. Phys. 368: 315-332 (2018) - [j25]Varun Shankar, Akil Narayan, Robert M. Kirby:
RBF-LOI: Augmenting Radial Basis Functions (RBFs) with Least Orthogonal Interpolation (LOI) for solving PDEs on surfaces. J. Comput. Phys. 373: 722-735 (2018) - [j24]Ben Adcock, Anyi Bao, John D. Jakeman, Akil Narayan:
Compressed Sensing with Sparse Corruptions: Fault-Tolerant Sparse Collocation Approximations. SIAM/ASA J. Uncertain. Quantification 6(4): 1424-1453 (2018) - [j23]Ling Guo, Akil Narayan, Liang Yan, Tao Zhou:
Weighted Approximate Fekete Points: Sampling for Least-Squares Polynomial Approximation. SIAM J. Sci. Comput. 40(1) (2018) - [j22]Vahid Keshavarzzadeh, Robert M. Kirby, Akil Narayan:
Numerical Integration in Multiple Dimensions with Designed Quadrature. SIAM J. Sci. Comput. 40(4): A2033-A2061 (2018) - [c1]Roohallah Khatami, Masood Parvania, Pramod P. Khargonekar, Akil Narayan:
Continuous-Time Stochastic Modeling and Estimation of Electricity Load. CDC 2018: 3988-3993 - [i4]Vahid Keshavarzzadeh, Robert M. Kirby, Akil Narayan:
Numerical Integration in Multiple Dimensions with Designed Quadrature. CoRR abs/1804.06501 (2018) - [i3]Varun Shankar, Akil Narayan, Robert M. Kirby:
RBF-LOI: Augmenting Radial Basis Functions (RBFs) with Least Orthogonal Interpolation (LOI) for Solving PDEs on Surfaces. CoRR abs/1807.02775 (2018) - [i2]Vahid Keshavarzzadeh, Robert M. Kirby, Akil Narayan:
Parametric Topology Optimization with Multi-Resolution Finite Element Models. CoRR abs/1808.10367 (2018) - [i1]Daniel J. Perry, Robert M. Kirby, Akil Narayan, Ross T. Whitaker:
Allocation strategies for high fidelity models in the multifidelity regime. CoRR abs/1812.11601 (2018) - 2017
- [j21]Lun Yang, Akil Narayan, Peng Wang:
Sequential data assimilation with multiple nonlinear models and applications to subsurface flow. J. Comput. Phys. 346: 356-368 (2017) - [j20]Jiahua Jiang, Yanlai Chen, Akil Narayan:
Offline-Enhanced Reduced Basis Method Through Adaptive Construction of the Surrogate Training Set. J. Sci. Comput. 73(2-3): 853-875 (2017) - [j19]Pranay Seshadri, Akil Narayan, Sankaran Mahadevan:
Effectively Subsampled Quadratures for Least Squares Polynomial Approximations. SIAM/ASA J. Uncertain. Quantification 5(1): 1003-1023 (2017) - [j18]Akil Narayan, John D. Jakeman, Tao Zhou:
A Christoffel function weighted least squares algorithm for collocation approximations. Math. Comput. 86(306): 1913-1947 (2017) - [j17]Matt Feiszli, Akil Narayan:
Numerical Computation of Weil-Peterson Geodesics in the Universal Teichmüller Space. SIAM J. Imaging Sci. 10(3): 1322-1345 (2017) - [j16]Ling Guo, Akil Narayan, Tao Zhou, Yuhang Chen:
Stochastic Collocation Methods via ℓ1 Minimization Using Randomized Quadratures. SIAM J. Sci. Comput. 39(1) (2017) - [j15]John D. Jakeman, Akil Narayan, Tao Zhou:
A Generalized Sampling and Preconditioning Scheme for Sparse Approximation of Polynomial Chaos Expansions. SIAM J. Sci. Comput. 39(3) (2017) - 2016
- [j14]Yanlai Chen, Sigal Gottlieb, Alfa R. H. Heryudono, Akil Narayan:
A Reduced Radial Basis Function Method for Partial Differential Equations on Irregular Domains. J. Sci. Comput. 66(1): 67-90 (2016) - [j13]Jiahua Jiang, Yanlai Chen, Akil Narayan:
A Goal-Oriented Reduced Basis Methods-Accelerated Generalized Polynomial Chaos Algorithm. SIAM/ASA J. Uncertain. Quantification 4(1): 1398-1420 (2016) - 2015
- [j12]Tao Zhou, Akil Narayan, Dongbin Xiu:
Weighted discrete least-squares polynomial approximation using randomized quadratures. J. Comput. Phys. 298: 787-800 (2015) - 2014
- [j11]Xueyu Zhu, Akil Narayan, Dongbin Xiu:
Computational Aspects of Stochastic Collocation with Multifidelity Models. SIAM/ASA J. Uncertain. Quantification 2(1): 444-463 (2014) - [j10]Sergey Kushnarev, Akil Narayan:
Approximating the Weil-Petersson Metric Geodesics on the Universal Teichmüller Space by Singular Solutions. SIAM J. Imaging Sci. 7(2): 900-923 (2014) - [j9]Akil Narayan, Claude Jeffrey Gittelson, Dongbin Xiu:
A Stochastic Collocation Algorithm with Multifidelity Models. SIAM J. Sci. Comput. 36(2) (2014) - [j8]Tao Zhou, Akil Narayan, Zhiqiang Xu:
Multivariate Discrete Least-Squares Approximations with a New Type of Collocation Grid. SIAM J. Sci. Comput. 36(5) (2014) - [j7]Akil Narayan, John D. Jakeman:
Adaptive Leja Sparse Grid Constructions for Stochastic Collocation and High-Dimensional Approximation. SIAM J. Sci. Comput. 36(6) (2014) - 2013
- [j6]Akil C. Narayan, Jan S. Hesthaven:
A generalization of the Wiener rational basis functions on infinite intervals, Part II - Numerical investigation. J. Comput. Appl. Math. 237(1): 18-34 (2013) - [j5]John D. Jakeman, Akil Narayan, Dongbin Xiu:
Minimal multi-element stochastic collocation for uncertainty quantification of discontinuous functions. J. Comput. Phys. 242: 790-808 (2013) - [j4]Akil Narayan, Dongbin Xiu:
Constructing Nested Nodal Sets for Multivariate Polynomial Interpolation. SIAM J. Sci. Comput. 35(5) (2013) - 2012
- [j3]Akil Narayan, Youssef M. Marzouk, Dongbin Xiu:
Sequential data assimilation with multiple models. J. Comput. Phys. 231(19): 6401-6418 (2012) - [j2]Akil Narayan, Dongbin Xiu:
Stochastic Collocation Methods on Unstructured Grids in High Dimensions via Interpolation. SIAM J. Sci. Comput. 34(3) (2012) - 2011
- [j1]Akil Narayan, Jan S. Hesthaven:
A generalization of the Wiener rational basis functions on infinite intervals: Part I-derivation and properties. Math. Comput. 80(275): 1557-1583 (2011)
Coauthor Index
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Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2025-01-24 17:15 CET by the dblp team
all metadata released as open data under CC0 1.0 license
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since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
