1 citations · 1 across the 3 of their papers we have counts for
6 papers
Neural Network Representation of Time Integrators
Rainald Löhner, Harbir Antil
Deep neural network (DNN) architectures are constructed that are the exact equivalent of explicit Runge-Kutta schemes for numerical time integration. The network weights and biases…
NINNs: Nudging Induced Neural Networks
Harbir Antil, Rainald Löhner, Randy Price
New algorithms called nudging induced neural networks (NINNs), to control and improve the accuracy of deep neural networks (DNNs), are introduced. The NINNs framework can be applie…
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…
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…
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…
Revisiting Calderon's Problem
Rainald Löhner, Harbir Antil
A finite element code for heat conduction, together with an adjoint solver and a suite of optimization tools was applied for the solution of Calderon's problem. One of the question…