48 citations · 74 across the 12 of their papers we have counts for
13 papers
Symmetrized Sinkhorn-Gibbs Inference for Oscillatory Inverse Problems
Gabriel Huerta, Mohammad Motamed
Oscillatory inverse problems often exhibit highly nonconvex discrepancy landscapes due to signal misalignment and cycle-skipping phenomena, posing significant challenges for uncert…
Residual Multi-Fidelity Neural Network Computing
Owen Davis, Mohammad Motamed, Raul Tempone
In this work, we consider the general problem of constructing a neural network surrogate model using multi-fidelity information. Motivated by error-complexity estimates for ReLU ne…
Non-degenerate Marginal-Likelihood Calibration with Application to Quantum Characterization
Mohammad Motamed, N. Anders Petersson
We propose a marginal likelihood strategy within the Kennedy-O'Hagan (KOH) Bayesian framework, where a Gaussian process (GP) models the discrepancy between a physical system and it…
Deterministic and Bayesian Characterization of Quantum Computing Devices
Zhichao Peng, Daniel Appelö, N. Anders Petersson +3
Motivated by the noisy and fluctuating behavior of current quantum computing devices, this paper presents a data-driven characterization approach for estimating transition frequenc…
Approximation Power of Deep Neural Networks: an explanatory mathematical survey
Owen Davis, Mohammad Motamed
This survey provides an in-depth and explanatory review of the approximation properties of deep neural networks, with a focus on feed-forward and residual architectures. The primar…
Hierarchical Low-Rank Approximation of Regularized Wasserstein Distance
Mohammad Motamed
Sinkhorn divergence is a measure of dissimilarity between two probability measures. It is obtained through adding an entropic regularization term to Kantorovich's optimal transport…
A multi-fidelity neural network surrogate sampling method for uncertainty quantification
Mohammad Motamed
We propose a multi-fidelity neural network surrogate sampling method for the uncertainty quantification of physical/biological systems described by ordinary or partial differential…
Wasserstein metric-driven Bayesian inversion with applications to signal processing
Mohammad Motamed, Daniel Appelo
We present a Bayesian framework based on a new exponential likelihood function driven by the quadratic Wasserstien metric. Compared to conventional Bayesian models based on Gaussia…
Fuzzy-Stochastic Partial Differential Equations
Mohammad Motamed
We introduce and study a new class of partial differential equations (PDEs) with hybrid fuzzy-stochastic parameters, coined fuzzy-stochastic PDEs. Compared to purely stochastic PDE…
A Fuzzy-Stochastic Multiscale Model for Fiber Composites: A one-dimensional study
Ivo Babuska, Mohammad Motamed
We study mathematical and computational models for computing the deformation of fiber-reinforced cross-plied laminates due to external forces. This requires an understanding of bot…
A Sparse Stochastic Collocation Technique for High-Frequency Wave Propagation with Uncertainty
Gabriela Malenova, Mohammad Motamed, Olof Runborg +1
We consider the wave equation with highly oscillatory initial data, where there is uncertainty in the wave speed, initial phase and/or initial amplitude. To estimate quantities of…
Fast Bayesian Optimal Experimental Design for Seismic Source Inversion
Quan Long, Mohammad Motamed, Raul Tempone
We develop a fast method for optimally designing experiments in the context of statistical seismic source inversion. In particular, we efficiently compute the optimal number and lo…
Taylor Expansion and Discretization Errors in Gaussian Beam Superposition
Mohammad Motamed, Olof Runborg
The Gaussian beam superposition method is an asymptotic method for computing high frequency wave fields in smoothly varying inhomogeneous media. In this paper we study the accuracy…