Jan Sickmann Hesthaven

Publications associées (256)

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Publications associées (256)

Population pharmacokinetic model selection assisted by machine learning

Jan Sickmann Hesthaven, Nadia Terranova

A fit-for-purpose structural and statistical model is the first major requirement in population pharmacometric model development. In this manuscript we discuss how this complex and computationally intensive task could benefit from supervised machine learni ...

SPRINGER/PLENUM PUBLISHERS2021

A non-intrusive multifidelity method for the reduced order modeling of nonlinear problems

Jan Sickmann Hesthaven, Mariella Kast, Mengwu Guo

We propose a non-intrusive reduced basis (RB) method for parametrized nonlinear partial differential equations (PDEs) that leverages models of different accuracy. From a collection of low-fidelity (LF) snapshots, parameter locations are extracted for the e ...

ELSEVIER SCIENCE SA2020

A physics-informed machine learning framework is developed for the reduced-order modeling of parametrized steady-state partial differential equations (PDEs). During the offline stage, a reduced basis is extracted from a collection of high-fidelity solution ...

2020

Controlling oscillations in spectral methods by local artificial viscosity governed by neural networks

Jan Sickmann Hesthaven, Deep Ray, Lukas Schwander

While a nonlinear viscosity is used widely to control oscillations when solving conservation laws using high-order elements based methods, such techniques are less straightforward to apply in global spectral methods as a local estimate of solution regulari ...

2020

Pointwise error estimate in difference setting for the two-dimensional nonlinear fractional complex Ginzburg-Landau equation

Jan Sickmann Hesthaven, Qifeng Zhang

In this paper, we propose a three-level linearized implicit difference scheme for the two-dimensional spatial fractional nonlinear complex Ginzburg-Landau equation. We prove that the difference scheme is uniquely solvable, stable and convergent under mild ...

2020

Controlling oscillations in high-order Discontinuous Galerkin schemes using artificial viscosity tuned by neural networks

Jan Sickmann Hesthaven, Niccolo' Discacciati, Deep Ray

High-order numerical solvers for conservation laws suffer from Gibbs phenomenon close to discontinuities, leading to spurious oscillations and a detrimental effect on the solution accuracy. A possible strategy to reduce it comprises adding a suitable amoun ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2020

Simulation-based Anomaly Detection and Damage Localization: An application to Structural Health Monitoring

Jan Sickmann Hesthaven, Caterina Bigoni

We propose a simulation-based decision strategy for the proactive maintenance of complex structures with a particular application to structural health monitoring (SHM). The strategy is based on a data-driven approach which exploits an offline-online decomp ...

2020

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In structural health monitoring (SHM), risk assessment and decision strategies rely primarily on sensor responses. Simulated data can be generated to emulate the monitoring phenomena under different natural operational and environmental conditions in order ...

2020

Fractional operators are widely used in mathematical models describing abnormal and nonlocal phenomena. Although there are extensive numerical methods for solving the corresponding model problems, theoretical analysis such as the regularity result, or the ...

SCIENCE PRESS2020

A Study of Several Artificial Viscosity Models within the Discontinuous Galerkin Framework

Jan Sickmann Hesthaven

Dealing with strong shocks while retaining low numerical dissipation traditionally has been one of the major challenges for high order methods like discontinuous Galerkin (DG). In the literature, shock capturing models have been designed for DG based on va ...

GLOBAL SCIENCE PRESS2020

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