Metamodeling Techniques For Evolutionary Optimization of Computationally Expensive Problems: Promises and Limitations. - Mohammed El-Beltagy; Prasanth B. Nair; Andy J. Keane (GECCO, 1999)

Some Greedy Algorithms for Sparse Nonlinear Regression. - Prasanth B. Nair; Arindam Choudhury 002; Andy J. Keane (ICML, 2001)

A Data Parallel Approach for Large-Scale Gaussian Process Modeling. - Arindam Choudhury 002; Prasanth B. Nair; Andy J. Keane (SDM, 2002)

Some Greedy Learning Algorithms for Sparse Regression and Classification with Mercer Kernels. - Prasanth B. Nair; Arindam Choudhury 002; Andy J. Keane (J. Mach. Learn. Res., 2002)

Global convergence of unconstrained and bound constrained surrogate-assisted evolutionary search in aerodynamic shape design. - Yew-Soon Ong; Kai Yew Lum; Prasanth B. Nair; Daming Shi 001; Z. K. Zhang (IEEE Congress on Evolutionary Computation, 2003)

Hierarchical surrogate-assisted evolutionary optimization framework. - Zongzhao Zhou; Yew-Soon Ong; Prasanth B. Nair (IEEE Congress on Evolutionary Computation, 2004)

Constructing a speculative kernel machine for pattern classification. - Arindam Choudhury 002; Prasanth B. Nair; Andy J. Keane (Neural Networks, 2006)

Max-min surrogate-assisted evolutionary algorithm for robust design. - Yew-Soon Ong; Prasanth B. Nair; Kai Yew Lum (IEEE Trans. Evol. Comput., 2006)

Pointwise uniformly convergent numerical treatment for the non-stationary Burger-Huxley equation using grid equidistribution. - Phani Chinchapatnam; Kamal Djidjeli; Prasanth B. Nair (Int. J. Comput. Math., 2007)

Predictive Haemodynamics in a One-Dimensional Human Carotid Artery Bifurcation. Part I: Application to Stent Design. - Vijaya B. Kolachalama; Neil W. Bressloff; Prasanth B. Nair; Clifford P. Shearman (IEEE Trans. Biomed. Eng., 2007)

Combining Global and Local Surrogate Models to Accelerate Evolutionary Optimization. - Zongzhao Zhou; Yew-Soon Ong; Prasanth B. Nair; Andy J. Keane; Kai Yew Lum (IEEE Trans. Syst. Man Cybern. Part C, 2007)

Hybrid evolutionary algorithm with Hermite radial basis function interpolants for computationally expensive adjoint solvers. - Yew-Soon Ong; Kai Yew Lum; Prasanth B. Nair (Comput. Optim. Appl., 2008)

Predictive Haemodynamics in a One-Dimensional Human Carotid Artery Bifurcation. Part II: Application to Graft Design. - Vijaya B. Kolachalama; Neil W. Bressloff; Prasanth B. Nair; Clifford P. Shearman (IEEE Trans. Biomed. Eng., 2008)

Genetic Programming Approaches for Solving Elliptic Partial Differential Equations. - Andras Sobester; Prasanth B. Nair; Andy J. Keane (IEEE Trans. Evol. Comput., 2008)

Voxel Based Adaptive Meshless Method for Cardiac Electrophysiology Simulation. - Phani Chinchapatnam; Kawal S. Rhode; Matthew Ginks; Prasanth B. Nair; Reza Razavi; Simon R. Arridge; Maxime Sermesant (FIMH, 2009)

Manifold learning for the emulation of spatial fields from computational models. - Wei W. Xing; Vasileios Triantafyllidis; Akeel A. Shah; Prasanth B. Nair; Nicholas Zabaras (J. Comput. Phys., 2016)

Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF). - Trefor W. Evans; Prasanth B. Nair (ICML, 2018)

Discretely Relaxing Continuous Variables for tractable Variational Inference. - Trefor W. Evans; Prasanth B. Nair (NeurIPS, 2018)

Exploiting Structure for Fast Kernel Learning. - Trefor W. Evans; Prasanth B. Nair (SDM, 2018)

Sparse radial basis function approximation with spatially variable shape parameters. - Valentin Stolbunov; Prasanth B. Nair (Appl. Math. Comput., 2018)

Some greedy algorithms for sparse polynomial chaos expansions. - Ricardo Baptista; Valentin Stolbunov; Prasanth B. Nair (J. Comput. Phys., 2019)

Weak Form Generalized Hamiltonian Learning. - Kevin Course; Trefor W. Evans; Prasanth B. Nair (NeurIPS, 2020)

Quadruply Stochastic Gaussian Processes. - Trefor W. Evans; Prasanth B. Nair (CoRR, 2020)

Amortized Reparametrization: Efficient and Scalable Variational Inference for Latent SDEs. - Kevin Course; Prasanth B. Nair (NeurIPS, 2023)

State estimation of a physical system with unknown governing equations. - Kevin Course; Prasanth B. Nair (Nat., 2023)

Robust Level-Set-Based Topology Optimization Under Uncertainties Using Anchored ANOVA Petrov-Galerkin Method. - Christophe Audouze; Aaron Klein; Adrian Butscher; Nigel J. W. Morris; Prasanth Nair 001; Masayuki Yano (SIAM/ASA J. Uncertain. Quantification, 2023)

Learning Stochastic Multiscale Models. - Andrew F. Ilersich; Prasanth B. Nair (CoRR, 2025)