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• Physics of the Inverted Harmonic Oscillator: From the lowest Landau level to event horizons

  Subramanyan, V., Hegde, S. S., Vishveshwara, S. & Bradlyn, B., Dec 2021, In: Annals of Physics. 435, 168470.

  Research output: Contribution to journal › Article › peer-review

  Open Access

• Physics of silicon nanodevices

  Ferry, D. K., Akis, R., Gilbert, M. J. & Ramey, S. M., Jan 1 2017, Silicon Nanoelectronics. CRC Press, p. 1-32 32 p.

  Research output: Chapter in Book/Report/Conference proceeding › Chapter

• Physics of sand castles: maximum angle of stability in wet and dry granular media

  Barabási, A. L., Albert, R. & Schiffer, P., Apr 15 1999, In: Physica A: Statistical Mechanics and its Applications. 266, 1-4, p. 366-371 6 p.

  Research output: Contribution to journal › Conference article › peer-review

• Physics of hollow Bose-Einstein condensates

  Padavić, K., Sun, K., Lannert, C. & Vishveshwara, S., Oct 2017, In: EPL. 120, 2, 20004.

  Research output: Contribution to journal › Article › peer-review

• Physics of failure, predictive modeling & data analytics for LOCA frequency

  O'Shea, N., Pence, J., Mohaghegh, Z. & Kee, E., May 8 2015, RAMS 2015 - 61st Annual Reliability and Maintainability Symposium, Proceedings and Tutorials 2015. Institute of Electrical and Electronics Engineers Inc., 7105125. (Proceedings - Annual Reliability and Maintainability Symposium; vol. 2015-May).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

• Physics of engineered protein hydrogels

  Kim, M., Tang, S. & Olsen, B. D., Apr 1 2013, In: Journal of Polymer Science, Part B: Polymer Physics. 51, 7, p. 587-601 15 p.

  Research output: Contribution to journal › Article › peer-review

• Physics of eccentric binary black hole mergers: A numerical relativity perspective

  Huerta, E. A., Haas, R., Habib, S., Gupta, A., Rebei, A., Chavva, V., Johnson, D., Rosofsky, S., Wessel, E., Agarwal, B., Luo, D. & Ren, W., Sep 4 2019, In: Physical Review D. 100, 6, 64003.

  Research output: Contribution to journal › Article › peer-review

  Open Access

• Physics of base charge dynamics in the three port transistor laser

  Then, H. W., Feng, M. & Holonyak, N., 2010, In: Applied Physics Letters. 96, 11, 113509.

  Research output: Contribution to journal › Article › peer-review

• Physics-motivated numerical solvers for partial differential equations

  San Martin, L. & Oono, Y., 1998, In: Physical Review E - Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics. 57, 4, p. 4795-4810 16 p.

  Research output: Contribution to journal › Article › peer-review

• Physics in the whirlwind of optical communications

  Thomas, G. A., Ackerman, D. A., Prucnal, P. R. & Lance Cooper, S., Sep 2000, Physics Today, 53, 9, p. 30-37 8 p.

  Research output: Contribution to specialist publication › Article

• Physics-informed neural networks for multiphysics data assimilation with application to subsurface transport

  He, Q. Z., Barajas-Solano, D., Tartakovsky, G. & Tartakovsky, A. M., Jul 2020, In: Advances in Water Resources. 141, 103610.

  Research output: Contribution to journal › Article › peer-review

• Physics-Informed Neural Network Method for Forward and Backward Advection-Dispersion Equations

  He, Q. Z. & Tartakovsky, A. M., Jul 2021, In: Water Resources Research. 57, 7, e2020WR029479.

  Research output: Contribution to journal › Article › peer-review

• Physics-informed machine learning with conditional Karhunen-Loève expansions

  Tartakovsky, A. M., Barajas-Solano, D. A. & He, Q., Feb 1 2021, In: Journal of Computational Physics. 426, 109904.

  Research output: Contribution to journal › Article › peer-review

• Physics-informed machine learning model for battery state of health prognostics using partial charging segments

  Kohtz, S., Xu, Y., Zheng, Z. & Wang, P., Jun 1 2022, In: Mechanical Systems and Signal Processing. 172, 109002.

  Research output: Contribution to journal › Article › peer-review

• Physics-Informed Machine Learning Method for Large-Scale Data Assimilation Problems

  Yeung, Y. H., Barajas-Solano, D. A. & Tartakovsky, A. M., May 2022, In: Water Resources Research. 58, 5, e2021WR031023.

  Research output: Contribution to journal › Article › peer-review

  Open Access

• Physics-informed machine learning method for forecasting and uncertainty quantification of partially observed and unobserved states in power grids

  Tipireddy, R. & Tartakovsky, A., 2019, Proceedings of the 52nd Annual Hawaii International Conference on System Sciences, HICSS 2019. Bui, T. X. (ed.). IEEE Computer Society, p. 3438-3444 7 p. (Proceedings of the Annual Hawaii International Conference on System Sciences; vol. 2019-January).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

• Physics-informed machine learning for surrogate modeling of wind pressure and optimization of pressure sensor placement

  Zhu, Q., Zhao, Z. & Yan, J., Mar 2023, In: Computational Mechanics. 71, 3, p. 481-491 11 p.

  Research output: Contribution to journal › Article › peer-review

• Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges

  Xu, Y., Kohtz, S., Boakye, J., Gardoni, P. & Wang, P., Feb 2023, In: Reliability Engineering and System Safety. 230, 108900.

  Research output: Contribution to journal › Article › peer-review

• Physics-informed machine learning assisted uncertainty quantification for the corrosion of dissimilar material joints

  Bansal, P., Zheng, Z., Shao, C., Li, J., Banu, M., Carlson, B. E. & Li, Y., Nov 2022, In: Reliability Engineering and System Safety. 227, 108711.

  Research output: Contribution to journal › Article › peer-review

• Physics-informed Karhunen-Loéve and neural network approximations for solving inverse differential equation problems

  Li, J. & Tartakovsky, A. M., Aug 1 2022, In: Journal of Computational Physics. 462, 111230.

  Research output: Contribution to journal › Article › peer-review

• Physics-informed Gaussian Process Regression Model for Battery Management

  Kohtz, S. & Wang, P., 2022, IISE Annual Conference and Expo 2022. Ellis, K., Ferrell, W. & Knapp, J. (eds.). Institute of Industrial and Systems Engineers, IISE, (IISE Annual Conference and Expo 2022).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

• Physics-informed Gaussian process regression for states estimation and forecasting in power grids

  Tartakovsky, A. M., Ma, T., Barajas-Solano, D. A. & Tipireddy, R., Apr 1 2023, In: International Journal of Forecasting. 39, 2, p. 967-980 14 p.

  Research output: Contribution to journal › Article › peer-review

• Physics-informed ensemble learning for online joint strength prediction in ultrasonic metal welding

  Meng, Y. & Shao, C., Dec 1 2022, In: Mechanical Systems and Signal Processing. 181, 109473.

  Research output: Contribution to journal › Article › peer-review

• Physics-Informed Deep Neural Networks for Transient Electromagnetic Analysis

  Noakoasteen, O., Wang, S., Peng, Z. & Christodoulou, C., 2020, In: IEEE Open Journal of Antennas and Propagation. 1, 1, p. 404-412 9 p., 9158400.

  Research output: Contribution to journal › Article › peer-review

  Open Access

• Physics-Informed Deep Neural Networks for Learning Parameters and Constitutive Relationships in Subsurface Flow Problems

  Tartakovsky, A. M., Marrero, C. O., Perdikaris, P., Tartakovsky, G. D. & Barajas-Solano, D., May 1 2020, In: Water Resources Research. 56, 5, e2019WR026731.

  Research output: Contribution to journal › Article › peer-review

• Physics-informed CoKriging model of a redox flow battery

  Howard, A. A., Yu, T., Wang, W. & Tartakovsky, A. M., Sep 15 2022, In: Journal of Power Sources. 542, 231668.

  Research output: Contribution to journal › Article › peer-review

• Physics-informed CoKriging: A Gaussian-process-regression-based multifidelity method for data-model convergence

  Yang, X., Barajas-Solano, D., Tartakovsky, G. & Tartakovsky, A. M., Oct 15 2019, In: Journal of Computational Physics. 395, p. 410-431 22 p.

  Research output: Contribution to journal › Article › peer-review

• PHYSICS INFORMATION AIDED KRIGING USING STOCHASTIC SIMULATION MODELS

  Yang, X., Tartakovsky, G. & Tartakovsky, A. M., 2021, In: SIAM Journal on Scientific Computing. 43, 6, p. A3862-A3891

  Research output: Contribution to journal › Article › peer-review

• PHYSICS GUIDELINES FOR THE COMPACT IGNITION TOKAMAK.

  Sheffield, J., Dory, R. A., Houlberg, W. A., Uckan, N. A., Bell, M., Colestock, P., Hosea, J., Kaye, S., Petravic, M., Post, D., Scott, S. D., Young, K. M., Burrell, K. H., Ohyabu, N., Stambaugh, R., Greenwald, M., Liewer, P., Ross, D., Singer, C. & Weitznerh, H., Nov 1 1986, In: Fusion Technology. 10, 3, p. 481-490 10 p.

  Research output: Contribution to journal › Conference article › peer-review

• Physics-guided machine learning for 3-D quantitative quasi-static elasticity imaging

  Hoerig, C., Ghaboussi, J. & Insana, M. F., 2020, In: Physics in medicine and biology. 65, 6, 065011.

  Research output: Contribution to journal › Article › peer-review

• Physics exam preparation: A comparison of three methods

  Fakcharoenphol, W. & Stelzer, T., Mar 17 2014, In: Physical Review Special Topics - Physics Education Research. 10, 1, 010108.

  Research output: Contribution to journal › Article › peer-review

• Physics-Embedded Machine Learning for Efficient Modeling of High-Frequency Circuits

  Liu, Y., Li, H. & Jin, J. M., 2022, 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., p. 834-835 2 p. (2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

• Physics education research funding census

  Henderson, C., Barthelemy, R., Finkelstein, N. & Mestre, J., 2012, 2011 Physics Education Research Conference. p. 211-214 4 p. (AIP Conference Proceedings; vol. 1413).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

• Physics-driven regularization of deep neural networks for enhanced engineering design and analysis

  Nabian, M. A. & Meidani, H., Feb 2020, In: Journal of Computing and Information Science in Engineering. 20, 1, 011006-1.

  Research output: Contribution to journal › Article › peer-review

• Physics-driven modeling for aerothermodynamics

  Jo, S. M., Venturi, S., Munafò, A., Sharma, M. & Panesi, M., 2021, AIAA Aviation and Aeronautics Forum and Exposition, AIAA AVIATION Forum 2021. American Institute of Aeronautics and Astronautics Inc, AIAA, AIAA 2021-3143. (AIAA Aviation and Aeronautics Forum and Exposition, AIAA AVIATION Forum 2021).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

• Physics disabilities

  Berenbaum, M., Mar 1 2003, In: American Entomologist. 49, 1, p. 3-4 2 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

• Physics-Constrained Machine Learning for Reliability-Based Design Optimization

  Xu, Y. & Wang, P., 2023, 2023 Annual Reliability and Maintainability Symposium, RAMS 2023. Institute of Electrical and Electronics Engineers Inc., (Proceedings - Annual Reliability and Maintainability Symposium; vol. 2023-January).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

• Physics-constrained deep neural network method for estimating parameters in a redox flow battery

  He, Q. Z., Stinis, P. & Tartakovsky, A. M., Apr 30 2022, In: Journal of Power Sources. 528, 231147.

  Research output: Contribution to journal › Article › peer-review

• Physics-based repair rate curves for segmented pipelines subject to seismic excitations

  Iannacone, L. & Gardoni, P., 2023, In: Sustainable and Resilient Infrastructure. 8, 1, p. 121-141 21 p.

  Research output: Contribution to journal › Article › peer-review

• Physics-Based Probabilistic Models for the Reliability Analysis of Bridges

  Nocera, F., Tabandeh, A. & Gardoni, P., 2022, Proceedings of the 1st Conference of the European Association on Quality Control of Bridges and Structures - EUROSTRUCT 2021. Pellegrino, C., Faleschini, F., Zanini, M. A., Matos, J. C., Casas, J. R. & Strauss, A. (eds.). Springer, p. 285-294 10 p. (Lecture Notes in Civil Engineering; vol. 200 LNCE).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

• Physics-based probabilistic models: Integrating differential equations and observational data

  Tabandeh, A., Asem, P. & Gardoni, P., Nov 2020, In: Structural Safety. 87, 101981.

  Research output: Contribution to journal › Article › peer-review

• Physics-based probabilistic demand model and reliability analysis for reinforced concrete beams under blast loads

  Stochino, F., Tabandeh, A., Gardoni, P. & Sassu, M., Dec 1 2021, In: Engineering Structures. 248, 112932.

  Research output: Contribution to journal › Article › peer-review

• Physics-based probabilistic capacity models and fragility estimates for light wood frame shear walls

  Dunton, A. & Gardoni, P., Jun 15 2023, In: Engineering Structures. 285, 115966.

  Research output: Contribution to journal › Article › peer-review

• Physics based models for metal hydride particle morphology, distribution, and effective thermal conductivity

  Smith, K. C. & Fisher, T. S., 2009, Nanoscale Heat Transport - From Fundamentals to Devices. Materials Research Society, p. 78-83 6 p. (Materials Research Society Symposium Proceedings; vol. 1172).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

• Physics-based modeling for partial slip behavior of spherical contacts

  Eriten, M., Polycarpou, A. A. & Bergman, L. A., Sep 2010, In: International Journal of Solids and Structures. 47, 18-19, p. 2554-2567 14 p.

  Research output: Contribution to journal › Article › peer-review

• Physics-based modeling for fretting behavior of nominally flat rough surfaces

  Eriten, M., Polycarpou, A. A. & Bergman, L. A., May 15 2011, In: International Journal of Solids and Structures. 48, 10, p. 1436-1450 15 p.

  Research output: Contribution to journal › Article › peer-review

• Physics-based Machine Learning with Filtering for Failure Prognostics Partially Observable Dynamic Systems

  Kohtz, S. & Wang, P., 2022, 68th Annual Reliability and Maintainability Symposium, RAMS 2022. Institute of Electrical and Electronics Engineers Inc., (Proceedings - Annual Reliability and Maintainability Symposium; vol. 2022-January).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

• Physics-Based Graphics Models in 3D Synthetic Environments as Autonomous Vision-Based Inspection Testbeds

  Hoskere, V., Narazaki, Y. & Spencer, B. F., Jan 1 2022, In: Sensors. 22, 2, 532.

  Research output: Contribution to journal › Article › peer-review

  Open Access

• Physics-based fragility functions: Their mathematical formulation and use in the reliability and resilience analysis of transportation infrastructure

  Nocera, F., Tabandeh, A., Guidotti, R., Boakye, J. & Gardoni, P., Jan 1 2018, Routledge Handbook of Sustainable and Resilient Infrastructure. Taylor and Francis, p. 239-260 22 p.

  Research output: Chapter in Book/Report/Conference proceeding › Chapter

• Physics-based foundation for empirical mode decomposition

  Lee, Y. S., Tsakirtzis, S., Vakakis, A. F., Bergman, L. A. & McFarland, D. M., Dec 2009, In: AIAA journal. 47, 12, p. 2938-2963 26 p.

  Research output: Contribution to journal › Article › peer-review