activity
20162025
most citedModel selection for dynamical systems via sparse regression and information criteria

208 citations · 340 across the 9 of their papers we have counts for

collaborators

10 papers

q-bio.QM2025

Uncertainty Quantification of Bacterial Microcompartment Permeability

Andre Archer, Brett J. Palmero, Charlotte Abrahamson +4

Salmonella expresses bacterial microcompartments (MCPs) upon 1,2-propanediol exposure. MCPs are nanoscale protein-bound shells that encase enzymes for the cofactor-dependent 1,2-pr…

math.NA2021

Walking into the complex plane to "order" better time integrators

Jithin D. George, Samuel Y. Jung, Niall M. Mangan

Most numerical methods for time integration use real time steps. Complex time steps provide an additional degree of freedom, as we can select the magnitude of the step in both the…

math.DS2021★ 24 cited

Model selection of chaotic systems from data with hidden variables using sparse data assimilation

H. Ribera, S. Shirman, A. V. Nguyen +1

Many natural systems exhibit chaotic behaviour such as the weather, hydrology, neuroscience and population dynamics. Although many chaotic systems can be described by relatively si…

physics.optics2019★ 3 cited

Frequency comb generation at 800nm in waveguide array quantum well diode lasers

Chang Sun, Mark Dong, Niall M. Mangan +3

A traveling wave model for a semiconductor diode laser based on quantum wells is presented as well as a comprehensive theoretical model of the lasing dynamics produced by the inten…

physics.optics2019★ 8 cited

Stable Numerical Schemes for Nonlinear Dispersive Equations with Counter-Propagation and Gain Dynamics

Chang Sun, Niall Mangan, Mark Dong +3

We develop a stable and efficient numerical scheme for modeling the optical field evolution in a nonlinear dispersive cavity with counter propagating waves and complex, semiconduct…

math.DS2018★ 75 cited

Model selection for hybrid dynamical systems via sparse regression

Niall M Mangan, Travis Askham, Steven L Brunton +2

Hybrid systems are traditionally difficult to identify and analyze using classical dynamical systems theory. Moreover, recently developed model identification methodologies largely…

physics.optics2017★ 22 cited

Traveling Wave Model for Frequency Comb Generation in Single Section Quantum Well Diode Lasers

Mark Dong, Niall M. Mangan, J. Nathan Kutz +2

We present a traveling wave model for a semiconductor diode laser based on quantum wells. The gain model is carefully derived from first principles and implemented with as few phen…

physics.data-an2017★ 208 cited

Model selection for dynamical systems via sparse regression and information criteria

Niall M. Mangan, J. Nathan Kutz, Steven L. Brunton +1

We develop an algorithm for model selection which allows for the consideration of a combinatorially large number of candidate models governing a dynamical system. The innovation ci…

math.DS2016

Inferring biological networks by sparse identification of nonlinear dynamics

Niall M. Mangan, Steven L. Brunton, Joshua L. Proctor +1

Inferring the structure and dynamics of network models is critical to understanding the functionality and control of complex systems, such as metabolic and regulatory biological ne…

physics.flu-dyn2016

Framework for systematic flow manipulation by wind and hydrokinetic energy turbine arrays

Shreyas Mandre, Niall M. Mangan

Wind and hydrokinetic energy turbines are often constrained to locations where the available energy is limited by the operation of the turbines themselves. In two-dimensions, we de…