{"id":11,"date":"2020-01-07T18:46:33","date_gmt":"2020-01-07T22:46:33","guid":{"rendered":"http:\/\/sites.nd.edu\/macart-group\/?page_id=11"},"modified":"2026-05-01T09:21:49","modified_gmt":"2026-05-01T13:21:49","slug":"publications","status":"publish","type":"page","link":"https:\/\/sites.nd.edu\/macart-group\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"

Forthcoming<\/h3>\n

L. Nista, C. D. K. Schumann, T. Grenga, J. F. MacArt, A. Attili, H. Pitsch, “Dynamic mixed turbulence modeling using a super-resolution generative adversarial approach,” arXiv:2511.21879<\/a><\/p>\n

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D. Dehtyriov, J. F. MacArt, J. Sirignano, “oRANS: Online optimisation of RANS machine learning models with embedded DNS data generation,” arXiv:2510.02982<\/a><\/p>\n<\/div>\n

P. Kakka, S. Nidhan, R. Ranade, J. F. MacArt, \u201cSampling-based Distributed Training with Message Passing Neural Network,\u201d arXiv:2402.15106<\/a><\/p>\n<\/div>\n

J. Sirignano, J. F. MacArt, “Dynamic Deep Learning LES Closures: Online Optimization With Embedded DNS,” arXiv:2303.02338<\/a><\/p>\n

Journal Articles (Refereed)<\/h3>\n

2026<\/h4>\n

A. S. Nair, N. Singh, M. Panesi, J. Sirignano, J. F. MacArt, \u201cPhysics-Based Machine Learning Closures and Wall Models for Hypersonic Transition\u2013Continuum Boundary Layer Predictions<\/a>,\u201d\u00a0Physical Review Fluids<\/em> 11<\/strong> (2026), 033402<\/span><\/p>\n

X. Liu, T. Hickling, J. F. MacArt, “Active Control of Turbulent Airfoil Flows Using Adjoint-based Deep Learning<\/a>,”\u00a0AIAA Journal<\/em> 64<\/strong> (2026), pp. 2683\u20132699<\/p>\n

T. Hickling, J. F. MacArt, J. Sirignano, D. Waidmann, “OGF: An Online Gradient Flow Method for Optimizing the Statistical Steady-State Time Averages of Unsteady Turbulent Flows<\/a>,”\u00a0Journal of Computational Physics<\/em> 552 <\/strong>(2026), 114610<\/p>\n

N. Daultry Ball, J. F. MacArt, J. Sirignano, \u201cOnline Optimisation of Machine Learning Collision Models to Accelerate Direct Molecular Simulation of Rarefied Gas Flows<\/a>,\u201d\u00a0Journal of Computational Physics<\/em> 549 <\/strong>(2026), 114601<\/p>\n

2025<\/h4>\n

P. Kakka, J. F. MacArt, “Neural network-augmented eddy viscosity closures for turbulent premixed jet flames<\/a>,”\u00a0Combustion and Flame\u00a0<\/em>278<\/strong> (2025), 114241<\/p>\n

M. Kryger, J. F. MacArt, “Optimization of Second-Order Transport Models for Transition-Continuum Flows<\/a>,”\u00a0AIAA Journal<\/em> 63 <\/strong>(2025), pp. 4223\u20134233<\/p>\n

S. W. Suh, J. F. MacArt, L. N. Olson, J. B. Freund, “A TVD neural network closure and application to turbulent combustion<\/a>,”\u00a0Journal of Computational Physics<\/em>\u00a0523<\/strong> (2025), 113638<\/p>\n

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L. Nista, C. D. K. Schumann, P. Petkov, V. Pavlov, T. Grenga, J. F. MacArt, A. Attili, S. Markov, H. Pitsch, \u201cParallel implementation and performance of super-resolution generative adversarial network turbulence models for large-eddy simulation<\/a>,\u201d Computers & Fluids<\/em> 288<\/strong> (2025), 106498<\/p>\n

A. S. Nair, S. Barwey, P. Pal, J. F. MacArt, T. Arcomano, R. Maulik, \u201cUnderstanding Latent Timescales in Neural Ordinary Differential Equation Models for Advection-Dominated Dynamical Systems<\/a>,\u201d Physica D: Nonlinear Phenomena<\/em> 476<\/strong> (2025), 134650<\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n

2024<\/h4>\n

X. Liu, J. F. MacArt, “Adjoint-based machine learning for active flow control<\/a>,”\u00a0Physical Review Fluids\u00a0<\/em>9<\/strong> (2024), 013901<\/p>\n

L. Nista, C. D. K. Schumann, M. Bode, T. Grenga, J. F. MacArt, A. Attili, H. Pitsch, “Influence of adversarial training on super-resolution turbulence <\/span>reconstruction<\/span><\/a>,” Physical Review Fluids\u00a0<\/em>9 <\/strong>(2024), 064601<\/p>\n

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2023<\/h4>\n

A. Nair, J. Sirignano, M. Panesi, J. F. MacArt, “Deep Learning Closure of the Navier\u2013Stokes Equations for Transition-Continuum Flows<\/a>,”\u00a0AIAA Journal<\/em> 61 <\/strong>(2023), pp. 5484\u20135497<\/span><\/p>\n

J. Sirignano, J. F. MacArt, “Deep learning closure models for large-eddy simulation of flows around bluff bodies<\/a>,”\u00a0Journal of Fluid Mechanics<\/em>\u00a0966<\/strong> (2023), A26<\/p>\n

J. Sirignano, J. F. MacArt, K. Spiliopoulos, \u201cPDE-constrained models with neural network terms: Optimization and global convergence<\/a>,\u201d\u00a0Journal of Computational Physics\u00a0<\/em>481<\/strong> (2023), 112016<\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n

2021<\/h4>\n
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J. F. MacArt, J. Sirignano, J. B. Freund, \u201cEmbedded training of neural-network subgrid-scale turbulence models<\/a>,\u201d Physical Review Fluids<\/em>\u00a06<\/strong> (2021), 050502 (invited article)<\/span><\/p>\n

J. M. Wang, J. F. MacArt, J. B. Freund, \u201cFlow dynamics of laser-induced breakdown at a fuel\u2013oxidizer interface and its effect on ignition<\/a>,\u201d Combustion and Flame<\/em>\u00a0229<\/strong> (2021), 111375<\/p>\n

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J. F. MacArt, M. E. Mueller, \u201cDamk\u00f6hler number scaling of active cascade effects in turbulent premixed combustion<\/a>,\u201d Physics of Fluids<\/em> 33<\/strong> (2021), 035103\u00a0(invited article; Editor\u2019s Choice)<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

2020<\/h4>\n
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J. F. MacArt, J. M. Wang, P. P. Popov, J. B. Freund, “Detailed simulation of ignition, flame acceleration, and instability transition following a laser-induced breakdown<\/a>,” Proceedings of the Combustion Institute<\/em> 38<\/strong> (2020), p. 2341\u20132349<\/p>\n

J. Sirignano, J. F. MacArt, J. B. Freund, “DPM: A deep learning PDE augmentation method with application to large\u2013eddy simulation<\/a>,” Journal of Computational Physics<\/em>\u00a0423<\/strong> (2020), 109811<\/p>\n

J. Lee, J. F. MacArt, M. E. Mueller, “Heat release effects on the Reynolds stress budgets in turbulent premixed flames<\/a>,” Combustion and Flame<\/em>\u00a0216<\/strong> (2020), p. 1\u20138<\/p>\n<\/div>\n<\/div>\n<\/div>\n

2019<\/h4>\n
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J. F. MacArt, T. Grenga, M. E. Mueller, “Evolution of flame-conditioned velocity statistics in turbulent premixed jet flames at low and high Karlovitz numbers<\/a>,” Proceedings of the Combustion Institute\u00a0<\/em>37<\/strong> (2019), p. 2503\u20132510<\/p>\n

2018<\/h4>\n
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J. F. MacArt, T. Grenga, M. E. Mueller, “Effects of combustion heat release on velocity and scalar statistics in turbulent premixed jet flames at low and high Karlovitz numbers<\/a>,” Combustion and Flame<\/em> 191<\/strong> (2018), p. 468\u2013485<\/p>\n

T. Grenga, J. F. MacArt, M. E. Mueller, “Dynamic Mode Decomposition of a Direct Numerical Simulation of a Turbulent Premixed Planar Jet Flame: Convergence of the Modes<\/a>,” Combustion Theory and Modelling<\/em> 22<\/strong> (2018), p. 795\u2013811<\/p>\n

2016<\/h4>\n
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J. F. MacArt, M. E. Mueller, “Semi-implicit iterative methods for low Mach number turbulent reacting flows: Operator splitting versus approximate factorization<\/a>,” Journal of Computational Physics<\/em> 326<\/strong> (2016), p. 569\u2013595<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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Conference Papers<\/h3>\n
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M. Kryger, J. F. MacArt, \u201cAdjoint-Based Optimization of Second-Order Continuum Model for Momentum and Heat Transport in Transition-Continuum Flows<\/a>,\u201d AIAA SciTech Forum<\/em> (2025)<\/p>\n<\/div>\n<\/div>\n<\/div>\n

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X. Liu, T. Hickling, J. F. MacArt, \u201cActive Control of Turbulent Airfoil Flows Using Adjoint-based Deep Learning<\/a>,\u201d AIAA SciTech Forum<\/em> (2025)<\/p>\n<\/div>\n<\/div>\n<\/div>\n

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A. Nair, J. Sirignano, N. Singh, M. Panesi, J. F. MacArt, \u201cAnisotropic deep learning transport models for two-dimensional transition-continuum flows<\/a>,\u201d AIAA SciTech Forum<\/em> (2025)<\/p>\n<\/div>\n<\/div>\n<\/div>\n

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J. F. MacArt, \u201cMachine Learning-Augmented Kinetics for Shock-Tube Ignition Delay Using a Variational Approach<\/a>,\u201d AIAA SciTech Forum<\/em> (2025)<\/p>\n<\/div>\n<\/div>\n<\/div>\n

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E. West, J. F. MacArt, R. Munipalli, \u201cVariational Data Assimilation in Shock Tube Flows<\/a>,\u201d AIAA SciTech Forum<\/em> (2025)<\/p>\n<\/div>\n<\/div>\n<\/div>\n

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A. Nair, D. Waidmann, J. Sirignano, N. Singh, M. Panesi, J. F. MacArt, \u201cAdjoint-Trained Deep-Learning Closures of the Navier\u2013Stokes Equations for 2D Nonequilibrium Flows<\/a>,\u201d AIAA SciTech Forum<\/em> (2024)<\/p>\n<\/div>\n<\/div>\n<\/div>\n

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E. Monti, N. Singh, J. Sirignano, J. F. MacArt, M. Panesi, G. Gori, \u201cPhysics-constrained deep learning- based model for non-equilibrium flows<\/a>,\u201d AIAA SciTech Forum<\/em> (2024)<\/p>\n<\/div>\n<\/div>\n<\/div>\n

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T. Hickling, J. Sirignano, J. F. MacArt, \u201cLarge Eddy Simulation of Airfoil Flows Using Adjoint-Trained Deep Learning Closure Models<\/a>,\u201d AIAA SciTech Forum<\/em> (2024)<\/p>\n<\/div>\n<\/div>\n<\/div>\n

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N. Daultry Ball, M. Panesi, J. F. MacArt, J. Sirignano, \u201cAdjoint Optimization of the BGK Equation with an Embedded Neural Network for Reduced-Order Modeling of Hypersonic Flows<\/a>,\u201d AIAA SciTech Forum<\/em> (2024)<\/p>\n<\/div>\n<\/div>\n<\/div>\n

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A. Nair, J. Sirignano, M. Panesi, J. F. MacArt, \u201cEntropy-stable Deep Learning for Navier\u2013Stokes Predictions of Transitional-regime Flows<\/a>,\u201d AIAA SciTech Forum<\/em> (2023)<\/p>\n<\/div>\n<\/div>\n<\/div>\n

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J. F. MacArt, J. Sirignano, M. Panesi, \u201cDeep Learning Closure of the Navier\u2013Stokes Equations for Transitional Flows<\/a>,\u201d AIAA SciTech Forum<\/em> (2022)<\/span><\/p>\n

P. P. Popov, M. Nishihara, A. Munaf\u00f2, J. F. MacArt, G. S. Elliott, J. B. Freund, \u201cLaser-Induced Breakdown Ignition of Low-Pressure Hydrogen-Air Premixtures<\/a>,\u201d AIAA SciTech Forum<\/em> (2020)<\/span><\/p>\n

A. C. Nunno, B. A. Perry, J. F. MacArt, M. E. Mueller, \u201cData-Driven Dimension Reduction in Turbulent Combustion: Utility and Limitations<\/a>,\u201d AIAA SciTech Forum<\/em> (2019)<\/span><\/p>\n

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Research Briefs<\/h3>\n
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J. F. MacArt, M. E. Mueller, Scaling and modeling of heat-release effects on subfilter turbulence in premixed combustion<\/a>, Center for Turbulence Research Proceedings of the Summer Program<\/em> (2018), p. 299\u2013308<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Forthcoming L. Nista, C. D. K. Schumann, T. Grenga, J. F. MacArt, A. Attili, H. Pitsch, “Dynamic mixed turbulence modeling using a super-resolution generative adversarial approach,” arXiv:2511.21879 D. Dehtyriov, J. F. MacArt, J. Sirignano, “oRANS: Online optimisation of RANS machine learning models with embedded DNS data generation,” arXiv:2510.02982 P. Kakka, S. Nidhan, R. Ranade, J. […]<\/p>\n","protected":false},"author":3642,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"class_list":["post-11","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/sites.nd.edu\/macart-group\/wp-json\/wp\/v2\/pages\/11","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sites.nd.edu\/macart-group\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sites.nd.edu\/macart-group\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sites.nd.edu\/macart-group\/wp-json\/wp\/v2\/users\/3642"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.nd.edu\/macart-group\/wp-json\/wp\/v2\/comments?post=11"}],"version-history":[{"count":57,"href":"https:\/\/sites.nd.edu\/macart-group\/wp-json\/wp\/v2\/pages\/11\/revisions"}],"predecessor-version":[{"id":337,"href":"https:\/\/sites.nd.edu\/macart-group\/wp-json\/wp\/v2\/pages\/11\/revisions\/337"}],"wp:attachment":[{"href":"https:\/\/sites.nd.edu\/macart-group\/wp-json\/wp\/v2\/media?parent=11"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}