activity
20182021
most citedNovel Deep neural networks for solving Bayesian statistical inverse

6 citations · 13 across the 9 of their papers we have counts for

collaborators

20 papers

math.NA2021

Approximation of fractional harmonic maps

Harbir Antil, Sören Bartels, Armin Schikorra

This paper addresses the approximation of fractional harmonic maps. Besides a unit-length constraint, one has to tackle the difficulty of nonlocality. We establish weak compactness…

math.NA2021

Convergence Analysis of the Rank-Restricted Soft SVD Algorithm

Mahendra Panagoda, Tyrus Berry, Harbir Antil

The soft SVD is a robust matrix decomposition algorithm and a key component of matrix completion methods. However, computing the soft SVD for large sparse matrices is often impract…

cs.LG2021

Novel DNNs for Stiff ODEs with Applications to Chemically Reacting Flows

Thomas S. Brown, Harbir Antil, Rainald Löhner +2

Chemically reacting flows are common in engineering, such as hypersonic flow, combustion, explosions, manufacturing processes and environmental assessments. For combustion, the num…

math.NA20216 cited

Novel Deep neural networks for solving Bayesian statistical inverse

Harbir Antil, Howard C Elman, Akwum Onwunta +1

We consider the simulation of Bayesian statistical inverse problems governed by large-scale linear and nonlinear partial differential equations (PDEs). Markov chain Monte Carlo (MC…

math.OC2020

A Note on Multigrid Preconditioning for Fractional PDE-Constrained Optimization Problems

Harbir Antil, Andrei Dr{ă}g{ă}nescu, Kiefer Green

In this note we present a multigrid preconditioning method for solving quadratic optimization problems constrained by a fractional diffusion equation. Multigrid methods within the…

physics.soc-ph2020

High Fidelity Modeling of Aerosol Pathogen Propagation in Built Environments with Moving Pedestrians

Rainald Löhner, Harbir Antil

A high fidelity model for the propagation of pathogens via aerosols in the presence of moving pedestrians is proposed. The key idea is the tight coupling of computational fluid dyn…