activity
20192021
most citedNovel Deep neural networks for solving Bayesian statistical inverse

6 citations · 9 across the 5 of their papers we have counts for

collaborators

7 papers

math.OC20211 cited

Optimal Control, Numerics, and Applications of Fractional PDEs

Harbir Antil, Thomas S. Brown, Ratna Khatri +3

This article provides a brief review of recent developments on two nonlocal operators: fractional Laplacian and fractional time derivative. We start by accounting for several appli…

math.OC2021

Non-diffusive Variational Problems with Distributional and Weak Gradient Constraints

Harbir Antil, Rafael Arndt, Carlos N. Rautenberg +1

In this paper, we consider non-diffusive variational problems with mixed boundary conditions and (distributional and weak) gradient constraints. The upper bound in the constraint i…

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.AP2020

Moreau-Yosida regularization for optimal control of fractional PDEs with state constraints: parabolic case

Harbir Antil, Thomas S. Brown, Deepanshu Verma +1

This paper considers optimal control of fractional parabolic PDEs with both state and control constraints. The key challenge is how to handle the state constraints. Similarly, to t…

math.OC2020

Fractional Deep Neural Network via Constrained Optimization

Harbir Antil, Ratna Khatri, Rainald Löhner +1

This paper introduces a novel algorithmic framework for a deep neural network (DNN), which in a mathematically rigorous manner, allows us to incorporate history (or memory) into th…