Papers, organized by year of completion. Entries with only links to preprints are in various stages of submission and review with journals.


2021

  • Methodology for sensitivity analysis of homogenized cross-sections to instantaneous and historical lattice conditions with application to AP1000® PWR lattice
    D. Price, T. Folk, M. Duschenes, K. Garikipati, B. Kochunas
    Energies
    Vol. 14, 3378, 2021

  • Reduced order models from computed states of physical systems using non-local calculus on finite weighted graphs
    M. Duschenes, K. Garikipati
    [available on arXiv]

  • System inference via field inversion for the spatio-temporal progression of infectious diseases: Studies of COVID-19 in Michigan and Mexico
    Z. Wang, M. Carrasco-Teja, X. Zhang, G.H. Teichert, K. Garikipati
    Arch Computat Methods Eng
    Vol 28, 4283-4295, 2021, doi:10.1007/s11831-021-09643-1
    [available on arXiv] [available on medRxiv]


2020

  • Sensitivity of void mediated failure to geometric design features of porous metals
    G.H. Teichert, M. Khalil, C. Alleman, K. Garikipati, R.E. Jones
    [available on arXiv]

  • LixCoO2 phase stability studied by machine learning-enabled scale bridging between electronic structure, statistical mechanics and phase field theories
    G.H. Teichert, S. Das, M. Aykol, C. Gopal, V. Gavini, K. Garikipati
    [available on arXiv]

  • Biomembranes undergo complex, non-axisymmetric deformations governed by Kirchhoff-Love kinematics and revealed by a three dimensional computational framework
    D. Auddya*, X. Zhang*, R. Gulati, R. Vasan, K. Garikipati, P. Rangamani, S. Rudraraju
    Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, to appear
    [available on arXiv] [available on bioRxiv]

  • Bayesian neural networks for weak solution of PDEs with uncertainty quantification
    X. Zhang, K. Garikipati
    [available on arXiv]

  • The graph theoretic approach for nodal cross section parameterization
    B. Kochunas, K. Garikipati, M. Duschenes, T. Folk
    [available on arXiv]

  • CRIMSON: An Open-Source Software Framework for Cardiovascular Integrated Modelling and Simulation
    C.J. Arthurs, R. Khlebnikov, A. Melville, M. Marcan, A. Gomez, D. Dillon-Murphy, F. Cuomo, M. Silva Vieira, J. Schollenberger, S. R. Lynch, C. Tossas-Betancourt, K. Iyer, S. Hopper, E. Livingston, P. Youssefi, A. Noorani, S. Ben Ahmed, F. J. H. Nauta, T. M. J. van Bakel, Y. Ahmed, P. A. J. van Bakel, J. Mynard, P. Di Achille, H. Gharahi, K. D. Lau, V. Filonova, M. Aguirre, N. Nama, N. Xiao, S. Baek, K. Garikipati, O. Sahni, D. Nordsletten, C. A. Figueroa
    PLOS Computational Biology, to appear
    [available on bioRXiv]

  • Discovery of deformation mechanisms and constitutive response of soft material surrogates of biological tissue by full-field characterization and data-driven variational system identification
    Z. Wang, J.B. Estrada, E.M. Arruda, K. Garikipati
    Journal of the Mechanics and Physics of Solids
    Vol. 153, 104474, 2021
    [available on bioRXiv]

  • An inverse modelling study on the local volume changes during early growth of the fetal human brain
    Z. Wang, B. Martin, J. Johannes Weickenmeier, K. Garikipati
    Brain Multiphysics
    Vol 2, 100023, 2021. doi:10.1016/j.brain.2021.100023
    [available on bioRXiv]
    [YouTube]

  • High order, semi-implicit, energy stable schemes for gradient flows
    A. Zaitzeff, S. Esedoglu, K. Garikipati
    [available on arXiv]

  • System inference for the spatio-temporal evolution of infectious diseases: Michigan in the time of COVID-19
    Z. Wang, X. Zhang, G.H. Teichert, M. Carrasco-Teja, K. Garikipati
    Computational Mechanics
    Vol 66, 1177, 2020. doi:10.1007/s00466-020-01894-2
    [available on arXiv]


2019

  • Editorial: Special Issue on Uncertainty Quantification, Machine Learning, and Data-Driven Modeling of Biological Systems
    A.B Tepole, D. Nordsletten, K. Garikipati, E. Kuhl
    Computer Methods in Applied Mechanics and Engineering
    Vol 362, 112832, 2020, doi:10.1016/j.cma.2020.112832

  • Active learning workflows and integrable deep neural networks for representing the free energy functions of alloys
    G.H. Teichert, A.R. Natarajan, A. Van der Ven, K. Garikipati
    Computer Methods in Applied Mechanics and Engineering
    Vol 371, 113281, 2020, doi:10.1016/j.cma.2020.113281
    [available on arXiv]

  • A Perspective on Regression and Bayesian Approaches for System Identification of Pattern Formation Dynamics
    Z. Wang, B. Wu, K. Garikipati, X. Huan
    Theoretical and Applied Mechanics Letters
    Vol 10(3), 188-194, 2020, doi:10.1016/j.taml.2020.01.028
  • [available on arXiv]

  • Variational system identification of the partial differential equations governing microstructure evolution in materials: Inference over sparse and spatially unrelated data
    Z. Wang, X. Huan, K. Garikipati
    Computer Methods in Applied Mechanics and Engineering
    Vol 377, 113706, 2021, doi.org/10.1016/j.cma.2021.113706
    [available on arXiv]

  • Machine learning materials physics: Multi-resolution neural networks learn the free energy and nonlinear elastic response of evolving microstructures
    X. Zhang, K. Garikipati
    Computer Methods in Applied Mechanics and Engineering
    Vol 372, 113362, 2020, doi:10.1016/j.cma.2020.113362
    [available on arXiv]

  • Modeling strength and failure variability due to porosity in additively manufactured metals
    M. Khalil, G.H. Teichert, C. Alleman, N.M. Heckman, R.E. Jones, K. Garikipati, B.L. Boyce
    Computer Methods in Applied Mechanics and Engineering
    To appear
    [available on arXiv]

  • Multiscale modeling meets machine learning: What can we learn?
    M. Alber, A. Buganza Tepole, W. Cannon, S. De, S. Dura-Bernal, K. Garikipati, G. Karniadakis, W.W. Lytton, P. Perdikaris, L. Petzold, E. Kuhl
    Archives of Computational Methods in Engineering
    doi.org/10.1007/s11831-020-09405-5
    [available on arXiv]

  • Second order threshold dynamics schemes for two phase motion by mean curvature
    A. Zaitzeff, S. Esedoglu, K. Garikipati
    Journal of Computational Physics
    Vol 410, 109404, 2020, doi.org/10.1016/j.jcp.2020.109404
    [available on arXiv]

  • Integrating machine learning and multiscale modeling: perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences
    M. Alber, A.B. Tepole, W. Cannon, S. De, S. Dura-Bernal, K. Garikipati, G. Karniadakis, W.W. Lytton, P. Perdikaris, L. Petzold, E. Kuhl
    npj Digital Medicine
    Vol 2, Article number 115, 2019, doi:10.1038/s41746-019-0193-y
    [available on arXiv]

  • Variational extrapolation of implicit schemes for general gradient flows
    A. Zaitzeff, S. Esedoglu, K. Garikipati
    SIAM Journal of Numerical Analysis
    To appear
    [available on arXiv]

  • A mechanical model reveals that non-axisymmetric buckling lowers the energy barrier associated with membrane neck constriction
    R. Vasan, S. Rudraraju, M. Akamatsu, K. Garikipati, P. Rangamani
    Soft Matter
    doi: 10.1039/c9sm01494b
    [available on arXiv]

  • The Materials Research Platform: Defining the requirements from user stories
    M. Aykol, J.S. Hummelshoj, A. Anapolsky, K. Aoyagi, M.Z. Bazant, T. Bligaard, R.D. Braatz, S. Broderick, D. Cogswell, J. Dagdelen, W. Drisdell, E. Garcia, K. Garikipati, V. Gavini, W. Gent, L. Giordano, C.P. Gomes, R. Gomez-Bombarelli, C.B. Gopal, J.M. Gregoire, J.C. Grossman, P. Herring, L. Hung, T.F. Jaramillo, L. King, H-K. Kwon, R. Maekawa, A.M. Minor, J. Montoya, T. Mueller, C. Ophus, K. Rajan, R. Ramprasad, B. Rohr, D. Schweigert, Y. Shao-Horn, Y. Suga, S.K. Suram, V. Viswanathan, J.F. Whitacre, A.P. Willard, O. Wodo, C. Wolverton, B.D. Storey
    Matter
    Available online 27 November 2019, doi.org/10.1016/j.matt.2019.10.024

2018

  • A computational framework for the morpho-elastic development of molluskan shells by surface and volume growth
    S. Rudraraju, D.E. Moulton, R. Chirat, A. Goriely, K. Garikipati
    PLOS Computational Biology
    Cover page article. Vol 15(7), e1007213, 2019, doi.org/10.1371/journal.pcbi.1007213
    [available on arXiv]

  • Machine learning materials physics: Integrable deep neural networks enable scale bridging by learning free energy functions
    G.H. Teichert, A.R. Natarajan, A. Van der Ven, K. Garikipati
    Computer Methods in Applied Mechanics and Engineering
    Vol 353, 201-216, 2019, doi.org/10.1016/j.cma.2019.05.019
    [available on arXiv]

  • Variational system identification of the partial differential equations governing pattern-forming physics: Inference under varying fidelity and noise
    Z. Wang, X. Huan, K. Garikipati
    Computer Methods in Applied Mechanics and Engineering
    Vol 356, 44-74, 2019, doi.org/10.1016/j.cma.2019.07.007
    [available on arXiv]

  • A graph theoretic framework for representation, exploration and analysis on computed states of physical systems
    R. Banerjee, K. Sagiyama, G.H. Teichert, K. Garikipati
    Computer Methods in Applied Mechanics and Engineering
    Vol 351, 501-530, 2019, doi.org/10.1016/j.cma.2019.03.053
    [available on arXiv]

  • On the Voronoi implicit interface method
    A. Zaitzeff, S. Esedoglu, K. Garikipati
    To appear in SIAM Journal on Scientific Computing
    [available on arXiv]

  • PRISMS: An integrated, open-source framework for accelerating predictive structural materials science
    L. K. Aagesen, J. F. Adams, J. E. Allison, W. B. Andrews, V. Araullo-Peters, T. Berman, Z. Chen, S. Daly, S. Das, S. DeWitt, S. Ganesan, K. Garikipati, V. Gavini, A. Githens, M. Hedstrom, Z. Huang, H. V. Jagadish, J. W. Jones, J. Luce, E. A. Marquis, A. Misra, D. Montiel, P. Motamarri, A. D. Murphy, A. R. Natarajan, S. Panwar, B. Puchala, L. Qi, S. Rudraraju, K. Sagiyama, E. L. S. Solomon, V. Sundararaghavan, G. Tarcea, G. H. Teichert, J. C. Thomas, K. Thornton, A. Van der Ven, Z. Wang, T. Weymouth, C. Yang
    Journal of Materials
    August 2018, doi.org/10.1007/s11837-018-3079-6

  • Machine learning materials physics: Surrogate optimization and multi-fidelity algorithms predict precipitate morphology in an alternative to phase field dynamics
    G. Teichert, K. Garikipati
    Computer Methods in Applied Mechanics and Engineering
    Vol. 344: 666-693, 2019, doi.org/10.1016/j.cma.2018.10.025
    [available on arXiv]

  • A diffuse interface framework for modelling the evolution of multi-cell aggregates as a soft packing problem due to growth and division of cells
    J. Jiang, S. Rudraraju, K. Garikipati
    Bulletin of Mathematical Biology
    2019, doi.org/10.1007/s11538-019-00577-1
    [available on arXiv]

  • A multi-physics battery model with particle scale resolution of evolving porosity and electrolyte flow
    Z. Wang and K. Garikipati
    Journal of the Electrochemical Society
    Vol. 165: A2421-A438, 2018, doi:10.1149/2.0141811jes
    [available on arXiv]

  • Machine learning materials physics: Deep neural networks trained on elastic free energy data from martensitic microstructures predict homogenized stress fields with high accuracy
    K. Sagiyama, K. Garikipati
    [available on arXiv]


2017

  • A computational study of the mechanisms growth-driven folding patterns on shells, with application to the developing brain
    S.N. Verner, K. Garikipati
    Extreme Mechanics Letters
    Cover page article. Vol. 18: 58-69, January 2018, doi.org/10.1016/j.eml.2017.11.003
    [available on arXiv]

  • Unconditionally stable, second-order schemes for gradient-regularized, non-convex, finite-strain elasticity modeling martensitic phase transformations
    K. Sagiyama, K. Garikipati
    Computer Methods in Applied Mechanics and Engineering
    Vol. 338: 597-617, August 2018, doi.org/10.1016/j.cma.2018.04.036
    [available on arXiv]

  • A numerical study of branching and stability of solutions to three-dimensional martensitic phase transformations using gradient-regularized, non-convex, finite strain elasticity
    K. Sagiyama, S. Rudraraju, K. Garikipati
    [available on arXiv]

  • Perspectives on the mathematics of biological patterning and morphogenesis
    K. Garikipati
    Journal of the Mechanics and Physics of Solids
    Vol. 99: 192-210, 2017, doi:10.1016/j.jmps.2016.11.013
    [available on arXiv]

  • Intercalation driven porosity effects on the electro-chemo-thermo-mechanical response in continuum models for battery material electrodes
    Z. Wang, J. Siegel, K. Garikipati
    Journal of the Electrochemical Society
    Vol. 164: A2199-A2212, 2017, doi:10.1149/2.0081712jes
    [available on arXiv]

  • A variational treatment of material configurations with application to interface motion and microstructural evolution
    G. Teichert, S. Rudraraju, K. Garikipati
    Journal of the Mechanics and Physics of Solids
    Vol. 99: 338–356, 2017, doi:10.1016/j.jmps.2016.11.008
    [available on arXiv]

  • A comparison of Redlich-Kister polynomial and cubic spline representations of the chemical potential in phase field computations
    G. Teichert, H. Gunda, S. Rudraraju, A. Natarajan, B. Puchala, K. Garikipati, A. Van der Ven
    Computational Materials Science
    Vol. 128: 127-139, 2017, doi:10.1016/j.commatsci.2016.11.024
    [available on arXiv]

2016

  • The spatial patterning potential of nonlinear diffusion Comment on “Phase separation driven by density-dependent movement: A novel mechanism for ecological patterns” by Quan-Xing Liu et al.
    P.K. Maini, K. Garikipati
    Physics of Life Reviews
    Vol. 19: 128-130, 2016, doi:10.1016/j.plrev.2016.10.011

  • Multi-physics simulations of lithiation-induced stress in LiTiO electrode particles
    T. Jiang, S. Rudraraju, A. Roy, A. Van der Ven, K. Garikipati, M. L. Falk
    Journal of Physical Chemistry C
    Vol. 120: 27871–27881, 2016, doi:10.1021/acs.jpcc.6b09775
    [available on arXiv]

  • Coordination of signaling and tissue mechanics during morphogenesis of murine intestinal villi: a role for mitotic cell rounding
    A.M. Freddo, S.K. Shoffner, Y. Shao, K. Taniguchi, A. S. Grosse, M.N. Guysinger, S. Wang, S. Rudraraju, B. Margolis, K. Garikipati, S. Schnell and D.L. Gumucio
    Integrative Biology
    Vol. 8: 918-928, 2016, doi:10.1039/C6IB00046K

  • Unconditionally stable, second-order accurate schemes for solid state phase transformations driven by mechano-chemical spinodal decomposition
    K. Sagiyama, S. Rudraraju, K. Garikipati
    Computer Methods in Applied Mechanics and Engineering
    Vol. 311: 556–575, 2016, doi: 10.1016/j.cma.2016.09.003
    [available on arXiv]

  • Mechano-chemical spinodal decomposition: A phenomenological theory of phase transformations in multi-component, crystalline solids
    S. Rudraraju, A. Van der Ven, K. Garikipati
    Nature npj Computational Materials
    Article number: 16012, 2016, doi:10.1038/npjcompumats.2016.12
    [available on arXiv]

  • A three dimensional field formulation, and isogeometric solutions to point and line defects using Toupin's theory of gradient elasticity at finite strains
    Z. Wang, S. Rudraraju, K. Garikipati
    Journal of the Mechanics and Physics of Solids
    Vol. 94: 336-361, 2016, doi:10.1016/j.jmps.2016.03.028
    [available on arXiv]

2015


2014

  • Rate dependence of swelling in lithium ion cells
    K. Y. Oh, J. B. Siegel, L. Secondo, S. U. Kim, N. A. Samad, J. W. Qin, D. Anderson, K. Garikipati, A., Knobloch, B. I. Epureanu, C. W. Monroe and A. Stefanopoulou
    Journal of Power Sources
    Vol 267: 197-202, 2014

  • Elastic free energy drives the shape of prevascular tumors
    K.L. Mills, S. Rudraraju, R. Kemkemer, K. Garikipati
    PLoS ONE
    Vol 9(7):e103245, doi:10.1371/journal.pone.0103245 2014
    available on arXiv

  • Three dimensional iso-geometric solutions to general boundary value problems of Toupin's theory of gradient elasticity at finite strains
    S. Rudraraju, A. Van der Ven, K. Garikipati
    Computer Methods in Applied Mechanics and Engineering
    Vol 278: 705-728, 2014
    available on arXiv

  • A computational study of stress fiber-focal adhesion dynamics governing cell contractility
    M. Maraldi, C. Valero, K. Garikipati
    Biophysical Journal
    Vol. 106: 1890-1901, 2014, doi:10.1016/j.bpj.2014.03.027
    available on arXiv

2012

  • Predictions of crack propagation using a variational multiscale approach and its application to fracture in laminated fiber reinforced composites
    S. Rudraraju, A. Salvi, K. Garikipati, A.M. Waas
    Composite Structures
    Vol. 94: 3336–3346, 2012, doi: 10.1016/j.compstruct.2012.03.035

  • Experimental observations and numerical simulations of curved crack propagation in laminated fiber composites
    S. Rudraraju, A. Salvi, K. Garikipati, A.M. Waas
    Composites Science and Technology
    Vol. 72: 1064-1074., 2012, doi:10.1016/j.compscitech.2011.07.020

2011

  • p38Gamma Promotes breast cancer cell motility and metastasis through regulation of RhoC GTPase, cytoskeletal architecture, and a novel leading edge behavior
    D.T. Rosenthal, H. Iyer, S. Escudero, L. Bao, Z. Wu, A.C. Ventura, C.G. Kleer, E.M. Arruda, K. Garikipati, S.D. Merajver
    Cancer Research
    Vol 71: 6338-6349, 2011, doi:10.1158/0008-5472.CAN-11-1291

  • Experimental characterization of tumor spheroids for studies of the energetics of tumor growth
    K.L. Mills, K. Garikipati, R. Kemkemer
    International Journal of Materials Research
    Vol. 7: 889-895., 2011, doi:10.3139/146.110532

  • Perspectives on biological growth and remodeling
    D. Ambrosi, G.A. Ateshian, E.M. Arruda, S.C. Cowin, J. Dumais, A. Goriely, G.A. Holzapfel, J.D. Humphrey, R. Kemkemer, E. Kuhl, J.E. Olberding, L.A. Taber, K. Garikipati
    Journal of the Mechanics and Physics of Solids
    Vol. 59(4): 863-883., 2011, doi:10.1016/j.jmps.2010.12.011

2010

  • The non-equilibrium thermodynamics and kinetics of focal adhesion dynamics
    J.E. Olberding, M.D. Thouless, E.M. Arruda, K. Garikipati
    PLoS ONE
    Vol. 5(8): e12043., 2010, DOI 10.1371/journal.pone.0012043

  • In silico estimates of the free energy rates in growing tumor spheroids
    H. Narayanan, S.N. Verner, K.L. Mills, R. Kemkemer, K. Garikipati
    Journal of Physics: Condensed Matter, (Special Issue on Cell-Substrate Interactions)
    Vol. 22(19) 194122, 2010, DOI 10.1088/0953-8984/22/19/194122
    available on arXiv

  • An energy basin finding algorithm for kinetic Monte Carlo acceleration
    B. Puchala, M.L. Falk, K. Garikipati
    Journal of Chemical Physics
    Vol. 132, 134104, 2010, DOI 10.1063/1.3369627

  • In-plane fracture of laminated fiber reinforced composites with varying fracture resistance: Experimental observations and numerical crack propagation simulations
    S.S. Rudraraju, A. Salvi, K. Garikipati, A.M. Waas
    International Journal of Solids and Structures
    Vol. 47(7-8), 901 — 911, 2010, DOI 10.1016/j.ijsolstr.2009.12.006

2009

  • The role of coherency strains on phase stability in LixFePO4: Needle crystallites minimize coherency strain and over-potential
    A. van der Ven, K. Garikipati, S. Kim, M. Wagemaker
    Journal of the Electrochemical Society
    Vol. 156(11) A949 — A957, 2009, DOI 10.1149/1.3222746

  • The micromechanics of fluid-solid interactions during growth in porous soft biological tissue
    H. Narayanan, E. M. Arruda, K. Grosh, K. Garikipati
    Biomechanics and Modeling in Mechanobiology
    Vol. 8(3), pp 167 — 181, 2009, DOI 10.1007/s10237-008-0126-1
    available on arXiv

  • The kinematics of biological growth
    K. Garikipati
    Applied Mechanics Review
    Vol. 62(3), Article no. 030801, 2009, DOI 10.1115/1.3090829

2008


2007


2006


2005


2004