Liu, T., Boullé, N., Sarfati, R., Earls, C.J. (2024) “LLMs learn governing principles of dynamical systems, revealing an in-context neural scaling law,” IN REVIEW arXiv:2402.00795
Stephany, R., Earls, C.J. (2024) “Weak -PDE-LEARN: A weak form based approach to discovering PDEs from noisy, limited data,” Journal of Computational Physics, Elsevier, Vol. 506, 112950 arXiv:2309.04699
Stephany, R., Earls, C.J. (2024) “PDE-LEARN: Using deep learning to discover partial differential equations from noisy, limited data,” Neural Networks, Elsevier, Vol. 174, 106242, pp. 1-16 arXiv:2212.04971
Praveen, H., Boullé, N., Earls, C.J. (2023) “Principled interpolation of Green’s functions learned from data,” Computer Methods in Applied Mechanics and Engineering, Elsevier, Vol. 409, 115971 arXiv:2211.06299
Stephany, R., Earls, C.J. (2022) “PDE-READ: Human-readable partial differential equation discovery using deep learning,” Neural Networks, Elsevier, Vol. 154, pp. 360-382 arXiv:2111.00998
Bonneville, C., Earls, C.J. (2022) “Bayesian deep learning for partial differential equation parameter discovery with sparse and noisy data,” Journal of Computational Physics, Elsevier, Vol. 16, 100115 arXiv:2108.04085
Boullé, N., Earls, C.J., Townsend, A. (2022) “Data-driven discovery of Green’s functions with human-understandable deep learning,” Scientific Reports, Vol. 12, No. 4824, Springer Nature.
Wu, W., Earls, C.J. (2022) “A new engineering theory describing oblique free surface impact by flexible plates,” Ocean Engineering, Vol. 256, Elsevier, 111473 arXiv:2103.08012
van Ede, M.S., Earls, C.J., Fichtner, A., Angst, U. (2021) “Electrochemical tomography as a nondestructive technique to study localized corrosion of metals,” NPJ Material Degradation, Vol. 5 No. 58, Springer Nature.
Lin, Y.C., Earls, C.J. (2021) “Validation experiment of a single-view image-sequence algorithm to identify scale and sea-state characteristics,” in IEEE Journal of Oceanic Engineering, doi: 10.1109/JOE.2020.3045645.
Bonneville, C., Wu, W., Earls, C.J. (2021) “A principled approach to design using high fidelity fluid-structure interaction simulations,” Finite Elements in Analysis and Design, Vol. 194, Elsevier, 103562.
Bonneville, C., Jenquin, M., Londono, J., Kelly, A., Cipolla, J., Earls, C.J. (2021) “Gaussian processes for shock test emulation,” Reliability Engineering and System Safety, Vol. 212, Elsevier, 107624.
Chavis III, J.T., Cochran, A. L., Earls, C. J. (2021) “CU-MSDSp: A flexible parallelized reversible jump Markov chain Monte Carlo method,” SoftwareX, Vol. 14, Elsevier, 100664.
Loeb, A., Earls, C.J. (2021) “Bayesian inference approaches for the detection and characterization of hidden pitting corrosion,” Mechanical Systems and Signal Processing, Vol. 154, Elsevier, 107545.
Sit, H., Earls, C.J. (2021) “Deep learning for classifying and characterizing atmospheric ducting within the maritime setting,” Computers and Geosciences , Vol. 157, Elsevier, 107545.
Sit, H., Earls, C.J. (2020) “Gaussian process regression for estimating EM ducting within the Marine Atmospheric Boundary Layer,” Radio Science, American Geophysical Union, Vol. 55, Issue 6, pp. 1-14.
Wu, W., Kosianka, J., Reed, H., Stull, C., Earls, C. (2020) “CU-BENs: A dedicated structural modeling finite element library,” SoftwareX, Vol. 11, Elsevier, pp. 1-5.
Sit, H., Earls, C.J. (2019) “Characterizing evaporation ducts within the marine atmospheric boundary layer using artificial neural networks,” Radio Science, American Geophysical Union, Vol. 54, Issue 12, pp. 1181-1191.
Gilles, M.A., Earls, C.J., Bindel, D. (2019) “A subspace pursuit method to infer refractivity in the Marine Atmospheric Boundary Layer,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 57, No. 8, pp. 5606-5617.
Loeb, A., Earls, C.J. (2019) “Analysis of heterogeneous computing approaches to simulating heat transfer in heterogenous materials,” Journal of Parallel and Distributed Computing, Vol. 133, Elsevier, pp. 1-17.
Lin, Y.C., Earls, C.J., Park, J.T., Smith, T.C. (2019) “Muti-parameter stochastic inversion for first and second moment mass properties of a model-scale ship with topside ice accumulation,” Applied Ocean Research, Vol. 82, Elsevier, pp. 143-157.
Lin, Y.C., Earls, C.J., Park, J.T., Smith, T.C. (2017) “Stochastic inversion for the roll gyradius second moment mass property in ships at full-scale and model-scale,” Applied Ocean Research, Vol. 63, Elsevier, pp. 24-35.
Sternfels, R., Earls, C.J. (2016) “The parameter space as a Riemannian manifold: effective sampling strategies for reduced order modeling,” Pre-print, Research Gate, DOI:10.13140/RG.2.1.3580.0722
Fountoulakis, V., Earls, C.J. (2016) “Duct heights inferred from radar sea clutter using proper orthogonal bases,” Radio Science, American Geophysical Union, Vol. 51, Issue 10, pp. 1614-1626.
Loeb, A., Earls, C.J. (2016) “Optimized inspection design for the thermographic characterization of sub-pixel sized through cracks,” Nondestructive Testing and Evaluation International, Vol. 82, Elsevier, pp. 44-55
Fountoulakis, V., Earls, C.J. (2016) “Inverting for maritime environments using proper orthogonal bases from sparsely sampled electro-magnetic propagation data,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 54, No. 12, pp. 7166-7176.
Reed, H.M., Earls, C.J. (2015) “Stochastic identification of the structural damage condition of a ship bow section under model uncertainty,” Ocean Engineering, Vol. 103, Elsevier, pp. 123-143.
Reed, H.M., Earls, C.J., Nichols, J.M. (2014) “Stochastic identification of imperfections in a submerged shell structure,” Computer Methods in Applied Mechanics and Engineering, Vol. 272, Elsevier, pp. 58-82.
Sternfels, R., Earls, C.J. (2013) “Reduced order model tracking and interpolation to solve PDE-based inverse problems,” Inverse Problems, Vol. 29, IOP Science, 075014.
Earls, C.J. (2013) “Bayesian inference of hidden corrosion in steel bridge connections: non-contact and sparse contact approaches,” Mechanical Systems and Signal Processing, Vol. 41, No. 1-2, Elsevier, pp. 420-432.
Reed, H.M., Nichols, J.M., Earls, C.J. (2013) “A modified differential evolution algorithm for damage identification in submerged shell structures,” Mechanical Systems and Signal Processing, Vol. 39, Elsevier, pp. 396-408.
Earls, C.J. (2012) “Stochastic inverse thermographic characterization of sub-pixel sized through cracks,” Mechanical Systems and Signal Processing, Vol. 30, Elsevier, pp. 146-156.
Jeong, W.Y., Earls, C.J., Philpot, W.D., Zehnder, A.T. (2012) “Inverse thermographic characterization of optically unresolvable through cracks in thin metal plates,” Mechanical Systems and Signal Processing, Vol. 27, Elsevier, pp. 634-650.
Guzas, E.L., Earls, C.J. (2011) “Simulating blast effects on steel beam-column members: Methods,” Computers and Structures, Vol. 89, No. 23-24, Elsevier, pp. 2133-2148.
Guzas, E.L., Earls, C.J. (2011) “Simulating blast effects on steel beam-column members: Applications,” Computers and Structures, Vol. 89, No. 23-24, Elsevier, pp. 2149-2161.
Stull, C.J., Nichols, J.M, Earls, C.J. (2011) “Stochastic inverse identification of geometric imperfections in shell structures,” Computer Methods in Applied Mechanics and Engineering, Vol. 200, Issue 25-28, Elsevier, pp. 2256-2267.
Stull, C.J. Earls, C.J., Koutsourelakis, P.S. (2011) “Model-based structural health monitoring of naval ships,” Computer Methods in Applied Mechanics and Engineering, Vol. 200, Issue 9-12, Elsevier, pp. 1137-1149.
Guzas, E.L., Earls, C.J. (2010) “Air blast load generation for simulating structural response,” Steel and Composite Structures, Vol. 10, No. 5, Techno-Press, Daejeon, Korea, pp. 429-455.
Stull, C.J., Earls, C.J. (2009) “A rapid assessment methodology for bridges damaged by truck strikes,” Steel & Composite Structures, Vol. 9, No. 3, Techno-Press, Daejeon, Korea, pp. 223-237.
Aquino, W., Brigham, J.C., Earls, C.J., Sukumar, N. (2009) “Generalized finite element formulation using proper orthogonal decomposition,” International Journal for Numerical Methods in Engineering, Vol. 79, Wiley, pp. 887-906.
Stull, C.J., Earls, C.J., Aquino, W. (2008) “A posteriori initial imperfection identification in shell buckling problems,” Computer Methods in Applied Mechanics and Engineering, Vol. 198, Issue 2, Elsevier, pp. 260-268.
