49 citations · 102 across the 11 of their papers we have counts for
11 papers
Variational Inference for Nonlinear Inverse Problems via Neural Net Kernels: Comparison to Bayesian Neural Networks, Application to Topology Optimization
Vahid Keshavarzzadeh, Robert M. Kirby, Akil Narayan
Inverse problems and, in particular, inferring unknown or latent parameters from data are ubiquitous in engineering simulations. A predominant viewpoint in identifying unknown para…
Machine Learning in Heterogeneous Porous Materials
Marta D'Elia, Hang Deng, Cedric Fraces +21
The "Workshop on Machine learning in heterogeneous porous materials" brought together international scientific communities of applied mathematics, porous media, and material scienc…
GP-HMAT: Scalable, Gaussian Process Regression with Hierarchical Low-Rank Matrices
Vahid Keshavarzzadeh, Shandian Zhe, Robert M. Kirby +1
A Gaussian process (GP) is a powerful and widely used regression technique. The main building block of a GP regression is the covariance kernel, which characterizes the relationshi…
Robust Topology Optimization Using Multi-Fidelity Variational Autoencoders
Rini Jasmine Gladstone, Mohammad Amin Nabian, Vahid Keshavarzzadeh +1
Robust topology optimization (RTO), as a class of topology optimization problems, identifies a design with the best average performance while reducing the response sensitivity to i…
A bandit-learning approach to multifidelity approximation
Yiming Xu, Vahid Keshavarzzadeh, Robert M. Kirby +1
Multifidelity approximation is an important technique in scientific computation and simulation. In this paper, we introduce a bandit-learning approach for leveraging data of varyin…
Visualization of topology optimization designs with representative subset selection
Daniel J Perry, Vahid Keshavarzzadeh, Shireen Y Elhabian +3
An important new trend in additive manufacturing is the use of optimization to automatically design industrial objects, such as beams, rudders or wings. Topology optimization, as i…
Designed Quadrature to Approximate Integrals in Maximum Simulated Likelihood Estimation
Prateek Bansal, Vahid Keshavarzzadeh, Angelo Guevara +2
Maximum simulated likelihood estimation of mixed multinomial logit (MMNL) or probit models requires evaluation of a multidimensional integral. Quasi-Monte Carlo (QMC) methods such…
Parametric Topology Optimization with Multi-Resolution Finite Element Models
Vahid Keshavarzzadeh, Robert M. Kirby, Akil Narayan
We present a methodical procedure for topology optimization under uncertainty with multi-resolution finite element models. We use our framework in a bi-fidelity setting where a coa…
Generation of Nested Quadrature Rules for Generic Weight Functions via Numerical Optimization: Application to Sparse Grids
Vahid Keshavarzzadeh, Robert M. Kirby, Akil Narayan
We present a numerical framework for computing nested quadrature rules for various weight functions. The well-known Kronrod method extends the Gauss-Legendre quadrature by adding n…
Convergence Acceleration for Time Dependent Parametric Multifidelity Models
Vahid Keshavarzzadeh, Robert M. Kirby, Akil Narayan
We present a numerical method for convergence acceleration for multifidelity models of parameterized ordinary differential equations. The hierarchy of models is defined as trajecto…
Numerical Integration in Multiple Dimensions with Designed Quadrature
Vahid Keshavarzzadeh, Robert M. Kirby, Akil Narayan
We present a systematic computational framework for generating positive quadrature rules in multiple dimensions on general geometries. A direct moment-matching formulation that enf…