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Items: 1 to 50 of 75

1.

Data-driven discovery of coordinates and governing equations.

Champion K, Lusch B, Kutz JN, Brunton SL.

Proc Natl Acad Sci U S A. 2019 Oct 21. pii: 201906995. doi: 10.1073/pnas.1906995116. [Epub ahead of print]

2.

Discovering time-varying aerodynamics of a prototype bridge by sparse identification of nonlinear dynamical systems.

Li S, Kaiser E, Laima S, Li H, Brunton SL, Kutz JN.

Phys Rev E. 2019 Aug;100(2-1):022220. doi: 10.1103/PhysRevE.100.022220.

PMID:
31574688
3.

Sex-related differences in intrinsic brain dynamism and their neurocognitive correlates.

de Lacy N, McCauley E, Kutz JN, Calhoun VD.

Neuroimage. 2019 Aug 22;202:116116. doi: 10.1016/j.neuroimage.2019.116116. [Epub ahead of print]

PMID:
31446126
4.

Forecasting dengue fever in Brazil: An assessment of climate conditions.

Stolerman LM, Maia PD, Kutz JN.

PLoS One. 2019 Aug 8;14(8):e0220106. doi: 10.1371/journal.pone.0220106. eCollection 2019.

5.

Dynamic mode decomposition for multiscale nonlinear physics.

Dylewsky D, Tao M, Kutz JN.

Phys Rev E. 2019 Jun;99(6-1):063311. doi: 10.1103/PhysRevE.99.063311.

PMID:
31330631
6.

Putting a bug in ML: The moth olfactory network learns to read MNIST.

Delahunt CB, Kutz JN.

Neural Netw. 2019 Oct;118:54-64. doi: 10.1016/j.neunet.2019.05.012. Epub 2019 Jun 4.

PMID:
31228724
7.

Dual lineage tracing shows that glomerular parietal epithelial cells can transdifferentiate toward theĀ adult podocyte fate.

Kaverina NV, Eng DG, Freedman BS, Kutz JN, Chozinski TJ, Vaughan JC, Miner JH, Pippin JW, Shankland SJ.

Kidney Int. 2019 Sep;96(3):597-611. doi: 10.1016/j.kint.2019.03.014. Epub 2019 Mar 29.

PMID:
31200942
8.

Slow-gamma frequencies are optimally guarded against effects of neurodegenerative diseases and traumatic brain injuries.

Maia PD, Raj A, Kutz JN.

J Comput Neurosci. 2019 Aug;47(1):1-16. doi: 10.1007/s10827-019-00714-8. Epub 2019 Jun 4.

PMID:
31165337
9.

Multilevel Mapping of Sexual Dimorphism in Intrinsic Functional Brain Networks.

de Lacy N, McCauley E, Kutz JN, Calhoun VD.

Front Neurosci. 2019 Apr 5;13:332. doi: 10.3389/fnins.2019.00332. eCollection 2019.

10.

Model selection for hybrid dynamical systems via sparse regression.

Mangan NM, Askham T, Brunton SL, Kutz JN, Proctor JL.

Proc Math Phys Eng Sci. 2019 Mar;475(2223):20180534. doi: 10.1098/rspa.2018.0534. Epub 2019 Mar 6.

11.

Sparse identification of nonlinear dynamics for model predictive control in the low-data limit.

Kaiser E, Kutz JN, Brunton SL.

Proc Math Phys Eng Sci. 2018 Nov;474(2219):20180335. doi: 10.1098/rspa.2018.0335. Epub 2018 Nov 14.

12.

Biological Mechanisms for Learning: A Computational Model of Olfactory Learning in the Manduca sexta Moth, With Applications to Neural Nets.

Delahunt CB, Riffell JA, Kutz JN.

Front Comput Neurosci. 2018 Dec 19;12:102. doi: 10.3389/fncom.2018.00102. eCollection 2018.

13.

Deep learning for universal linear embeddings of nonlinear dynamics.

Lusch B, Kutz JN, Brunton SL.

Nat Commun. 2018 Nov 23;9(1):4950. doi: 10.1038/s41467-018-07210-0.

14.

Deterministic generation of single soliton Kerr frequency comb in microresonators by a single shot pulsed trigger.

Kang Z, Li F, Yuan J, Nakkeeran K, Kutz JN, Wu Q, Yu C, Wai PKA.

Opt Express. 2018 Jul 9;26(14):18563-18577. doi: 10.1364/OE.26.018563.

PMID:
30114034
15.

Dynamic mode decomposition for plasma diagnostics and validation.

Taylor R, Kutz JN, Morgan K, Nelson BA.

Rev Sci Instrum. 2018 May;89(5):053501. doi: 10.1063/1.5027419.

PMID:
29864814
16.

The control structure of the nematode Caenorhabditis elegans: Neuro-sensory integration and proprioceptive feedback.

Fieseler C, Kunert-Graf J, Kutz JN.

J Biomech. 2018 Jun 6;74:1-8. doi: 10.1016/j.jbiomech.2018.03.046. Epub 2018 Apr 19.

PMID:
29705349
17.

Example-Based Super-Resolution Fluorescence Microscopy.

Jia S, Han B, Kutz JN.

Sci Rep. 2018 Apr 23;8(1):5700. doi: 10.1038/s41598-018-24033-7.

18.

Modeling cognitive deficits following neurodegenerative diseases and traumatic brain injuries with deep convolutional neural networks.

Lusch B, Weholt J, Maia PD, Kutz JN.

Brain Cogn. 2018 Jun;123:154-164. doi: 10.1016/j.bandc.2018.02.012. Epub 2018 Mar 26.

PMID:
29597065
19.

Preventing Neurodegenerative Memory Loss in Hopfield Neuronal Networks Using Cerebral Organoids or External Microelectronics.

Morrison M, Maia PD, Kutz JN.

Comput Math Methods Med. 2017;2017:6102494. doi: 10.1155/2017/6102494. Epub 2017 Sep 5.

20.

Estimating Memory Deterioration Rates Following Neurodegeneration and Traumatic Brain Injuries in a Hopfield Network Model.

Weber M, Maia PD, Kutz JN.

Front Neurosci. 2017 Nov 9;11:623. doi: 10.3389/fnins.2017.00623. eCollection 2017.

21.

Symmetries Constrain Dynamics in a Family of Balanced Neural Networks.

Barreiro AK, Kutz JN, Shlizerman E.

J Math Neurosci. 2017 Oct 10;7(1):10. doi: 10.1186/s13408-017-0052-6.

22.

Model selection for dynamical systems via sparse regression and information criteria.

Mangan NM, Kutz JN, Brunton SL, Proctor JL.

Proc Math Phys Eng Sci. 2017 Aug;473(2204):20170009. doi: 10.1098/rspa.2017.0009. Epub 2017 Aug 30.

23.

Multistability and Long-Timescale Transients Encoded by Network Structure in a Model of C. elegans Connectome Dynamics.

Kunert-Graf JM, Shlizerman E, Walker A, Kutz JN.

Front Comput Neurosci. 2017 Jun 13;11:53. doi: 10.3389/fncom.2017.00053. eCollection 2017.

24.

Chaos as an intermittently forced linear system.

Brunton SL, Brunton BW, Proctor JL, Kaiser E, Kutz JN.

Nat Commun. 2017 May 30;8(1):19. doi: 10.1038/s41467-017-00030-8.

25.

Data-driven discovery of partial differential equations.

Rudy SH, Brunton SL, Proctor JL, Kutz JN.

Sci Adv. 2017 Apr 26;3(4):e1602614. doi: 10.1126/sciadv.1602614. eCollection 2017 Apr.

26.

Reaction time impairments in decision-making networks as a diagnostic marker for traumatic brain injuries and neurological diseases.

Maia PD, Kutz JN.

J Comput Neurosci. 2017 Jun;42(3):323-347. doi: 10.1007/s10827-017-0643-y. Epub 2017 Apr 10.

PMID:
28393281
27.

Compound effects of aging and experimental FSGS on glomerular epithelial cells.

Schneider RR, Eng DG, Kutz JN, Sweetwyne MT, Pippin JW, Shankland SJ.

Aging (Albany NY). 2017 Feb 17;9(2):524-546. doi: 10.18632/aging.101176.

28.

Spatiotemporal Feedback and Network Structure Drive and Encode Caenorhabditis elegans Locomotion.

Kunert JM, Proctor JL, Brunton SL, Kutz JN.

PLoS Comput Biol. 2017 Jan 11;13(1):e1005303. doi: 10.1371/journal.pcbi.1005303. eCollection 2017 Jan.

29.

Functionality and Robustness of Injured Connectomic Dynamics in C. elegans: Linking Behavioral Deficits to Neural Circuit Damage.

Kunert JM, Maia PD, Kutz JN.

PLoS Comput Biol. 2017 Jan 5;13(1):e1005261. doi: 10.1371/journal.pcbi.1005261. eCollection 2017 Jan.

30.

Inferring connectivity in networked dynamical systems: Challenges using Granger causality.

Lusch B, Maia PD, Kutz JN.

Phys Rev E. 2016 Sep;94(3-1):032220. Epub 2016 Sep 27.

PMID:
27739857
31.

Renin-Angiotensin-Aldosterone System Inhibition Increases Podocyte Derivation from Cells of Renin Lineage.

Lichtnekert J, Kaverina NV, Eng DG, Gross KW, Kutz JN, Pippin JW, Shankland SJ.

J Am Soc Nephrol. 2016 Dec;27(12):3611-3627. Epub 2016 Apr 14.

32.

Discovering governing equations from data by sparse identification of nonlinear dynamical systems.

Brunton SL, Proctor JL, Kutz JN.

Proc Natl Acad Sci U S A. 2016 Apr 12;113(15):3932-7. doi: 10.1073/pnas.1517384113. Epub 2016 Mar 28.

33.

Koopman Invariant Subspaces and Finite Linear Representations of Nonlinear Dynamical Systems for Control.

Brunton SL, Brunton BW, Proctor JL, Kutz JN.

PLoS One. 2016 Feb 26;11(2):e0150171. doi: 10.1371/journal.pone.0150171. eCollection 2016.

34.

Extracting spatial-temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition.

Brunton BW, Johnson LA, Ojemann JG, Kutz JN.

J Neurosci Methods. 2016 Jan 30;258:1-15. doi: 10.1016/j.jneumeth.2015.10.010. Epub 2015 Oct 31.

PMID:
26529367
35.

Nonlinear model reduction for dynamical systems using sparse sensor locations from learned libraries.

Sargsyan S, Brunton SL, Kutz JN.

Phys Rev E Stat Nonlin Soft Matter Phys. 2015 Sep;92(3):033304. doi: 10.1103/PhysRevE.92.033304. Epub 2015 Sep 10.

PMID:
26465583
36.

Diagnostic tools for evaluating the impact of Focal Axonal Swellings arising in neurodegenerative diseases and/or traumatic brain injury.

Maia PD, Hemphill MA, Zehnder B, Zhang C, Parker KK, Kutz JN.

J Neurosci Methods. 2015 Sep 30;253:233-43. doi: 10.1016/j.jneumeth.2015.06.022. Epub 2015 Jul 16.

PMID:
26188255
37.

A reaction-diffusion model of cholinergic retinal waves.

Lansdell B, Ford K, Kutz JN.

PLoS Comput Biol. 2014 Dec 4;10(12):e1003953. doi: 10.1371/journal.pcbi.1003953. eCollection 2014 Dec.

38.

Herpes simplex virus-2 genital tract shedding is not predictable over months or years in infected persons.

Dhankani V, Kutz JN, Schiffer JT.

PLoS Comput Biol. 2014 Nov 6;10(11):e1003922. doi: 10.1371/journal.pcbi.1003922. eCollection 2014 Nov.

39.

Low-dimensional functionality of complex network dynamics: neurosensory integration in the Caenorhabditis Elegans connectome.

Kunert J, Shlizerman E, Kutz JN.

Phys Rev E Stat Nonlin Soft Matter Phys. 2014 May;89(5):052805. Epub 2014 May 12.

PMID:
25353842
40.

Data-driven inference of network connectivity for modeling the dynamics of neural codes in the insect antennal lobe.

Shlizerman E, Riffell JA, Kutz JN.

Front Comput Neurosci. 2014 Aug 13;8:70. doi: 10.3389/fncom.2014.00070. eCollection 2014.

41.

Sensory biology. Flower discrimination by pollinators in a dynamic chemical environment.

Riffell JA, Shlizerman E, Sanders E, Abrell L, Medina B, Hinterwirth AJ, Kutz JN.

Science. 2014 Jun 27;344(6191):1515-8. doi: 10.1126/science.1251041.

PMID:
24970087
42.

Compromised axonal functionality after neurodegeneration, concussion and/or traumatic brain injury.

Maia PD, Kutz JN.

J Comput Neurosci. 2014 Oct;37(2):317-32. doi: 10.1007/s10827-014-0504-x. Epub 2014 Jun 12.

PMID:
24916135
43.

Identifying critical regions for spike propagation in axon segments.

Maia PD, Kutz JN.

J Comput Neurosci. 2014 Apr;36(2):141-55. doi: 10.1007/s10827-013-0459-3. Epub 2013 Jul 2.

PMID:
23818067
44.

Phase retrieval using nonlinear diversity.

Lu CH, Barsi C, Williams MO, Kutz JN, Fleischer JW.

Appl Opt. 2013 Apr 1;52(10):D92-6. doi: 10.1364/AO.52.000D92.

PMID:
23545987
45.

High-energy mode-locked fiber lasers using multiple transmission filters and a genetic algorithm.

Fu X, Kutz JN.

Opt Express. 2013 Mar 11;21(5):6526-37. doi: 10.1364/OE.21.006526.

PMID:
23482223
46.

High-Energy Passive Mode-Locking of Fiber Lasers.

Ding E, Renninger WH, Wise FW, Grelu P, Shlizerman E, Kutz JN.

Int J Opt. 2012;2012. pii: 354156. Epub 2012 Jan 18.

47.

Spatial distribution clamping of discrete spatial solitons due to three photon absorption in AlGaAs waveguide arrays.

Hudson DD, Kutz JN, Schibli TR, Christodoulides DN, Morandotti R, Cundiff ST.

Opt Express. 2012 Jan 30;20(3):1939-44. doi: 10.1364/OE.20.001939.

PMID:
22330434
48.

Dual transmission filters for enhanced energy in mode-locked fiber lasers.

Li F, Ding E, Kutz JN, Wai PK.

Opt Express. 2011 Nov 7;19(23):23408-19. doi: 10.1364/OE.19.023408.

PMID:
22109217
49.

Scaling Fiber Lasers to Large Mode Area: An Investigation of Passive Mode-Locking Using a Multi-Mode Fiber.

Ding E, Lefrancois S, Kutz JN, Wise FW.

IEEE J Quantum Electron. 2011;47(5):597-606.

50.

Dissipative soliton resonance in a passively mode-locked fiber laser.

Ding E, Grelu P, Kutz JN.

Opt Lett. 2011 Apr 1;36(7):1146-8. doi: 10.1364/OL.36.001146.

PMID:
21479011

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