Education

2016 - 2019: University of Florida, Gainesville, FL
PhD Mechanical Engineering: 3.68/4.00 GPA
Dissertation: Influence of objective functions on the identification of material model parameters from experimental data pdf.

2014 - 2016: Stellenbosch University, Stellenbosch, South Africa
MEng Mechanical Engineering: cum laude
Thesis: Obtaining non-linear orthotropic material models for pvc-coated polyester via inverse bubble inflation https://hdl.handle.net/10019.1/98627 pdf.

2009 - 2013: University of Colorado, Colorado Springs, CO
BSc Mechanical Engineering: 3.72/4.00 GPA with honor


Professional experience

Lawrence Livermore National Laboratory

2021 - Present: Research Staff Member
2020 - 2021: Postdoctoral Researcher

Create end-to-end machine learning workflows on state of the art HPCs and cloud resources. My biggest problems can generate > 10 TBs of data and require thousands of GPUs to train. Developed real-time machine learning visualization software to quickly analyze thousands of simulation results.

Sandia National Laboratories

2018 - 2020: Research & Development Graduate Intern

Worked in the Verification Validation (VV), Uncertainty Quantification (UQ), Credibility Process group. R&D of statistical methods to estimate extreme tail probabilities with a limited number of samples.

University of Florida

2016 - 2019: Research Assistant / Instructor

Applied optimization to find model parameters that match experimental data, and research on quantifying the difference between experimental data and numerical models. Created and taught a Python Programming course.

UTC Aerospace Systems

2013 - 2014: Manufacturing Engineer
2012: Manufacturing Engineer Intern

Worked directly with all OEM final assembly lines to interface with Product Engineering on issues related to manufacturing. Participated on Integrated Product Development Teams to improve manufacturing processes associated with specialty aircraft seats (pilot, flight attendant, and observer) for commercial platforms.

University of Colorado - Colorado Springs

2010 - 2013: Student Assistant IV

All-purpose IT wizard. Graphic design guru.


Publications

Peer-reviewed journal articles

  1. Sterbentz, D. M., Jekel, C. F., White, D., Rieben, R. N., & Belof, J. (2023). Linear shaped-charge jet optimization using machine learning methods. J. Appl. Phys. 134 (4): 045102. doi:10.1063/5.0156373 pdf

  2. Sterbentz, D. M., Jekel, C. F., White, D., Aubry, S., Lorenzana, H., & Belof, J. (2022). Design optimization for Richtmyer-Meshkov instability suppression at shock-compressed material interfaces. Physics of Fluids. 34 (8): 082109. doi:10.1063/5.0100100 pdf

  3. Beck, V. A., Wong, J. J., Jekel, C. F., Tortorelli, D. A., Baker, S. E., Duoss, E. B., & Worsley, M. A. (2021). Computational design of microarchitected porous electrodes for redox flow batteries. Journal of Power Sources, 512, 230453. doi:10.1016/j.jpowsour.2021.230453 pdf

  4. Jekel, C.F., Venter, G., Venter, M.P., Stander, N. and Haftka, R.T., 2018. Similarity measures for identifying material parameters from hysteresis loops using inverse analysis. International Journal of Material Forming, 12(3), 355-378. doi:10.1007/s12289-018-1421-8 pdf

  5. Jekel, C.F., Venter, G. and Venter, M.P., 2017. Modeling PVC-coated polyester as a hypoelastic non-linear orthotropic material. Composite Structures, 161, pp.51-64. doi:10.1016/j.compstruct.2016.11.019 pdf

  6. Jekel, C.F., Venter, G. and Venter, M.P., 2016. Obtaining a hyperelastic non-linear orthotropic material model via inverse bubble inflation analysis. Structural and Multidisciplinary Optimization, 54(4), pp.927-935. doi:10.1007/s00158-016-1456-8 pdf

Conference papers

  1. Jekel, C.F., Sterbentz, D.M., Aubry, S., Choi, Y., White, D.A. and Belof, J.L., 2022. Using conservation laws to infer deep learning model accuracy of Richtmyer-Meshkov instabilities, In ECCOMAS 2022. doi:10.23967/eccomas.2022.286 pdf

  2. Jekel, C.F., and Haftka, R.T., 2020. Weaponizing Favorite Test Functions for Testing Global Optimization Algorithms: An illustration with the Branin-Hoo Function. In 2020 AIAA AVIATION FORUM. doi:10.2514/6.2020-3132 pdf

  3. Jekel, C.F., and Haftka, R.T., 2020. Risk Allocation for Design Optimization with Unidentified Statistical Distributions. In 2020 AIAA Non-Deterministic Approaches Conference. doi:0.2514/6.2020-0415 pdf

  4. Jekel, C. F., Haftka, R. T., Venter, M. P., and Venter, G. Cross Validation to Select Material Models with Bulge Inflation Tests on PVC-coated Polyester. Structural Engineering, Mechanics and Computation, September 2019. pdf

  5. Jekel, C. F., Grechuk, B., Zhang, Y., and Haftka, R. Comparison of Chebyshev’s Inequality and Non-parametric B-Basis to Estimate Failure Strength of Composite Open Hole Tension Tests. The World Congress of Structural and Multidisciplinary Optimization, May 2019. pdf

  6. Jekel, C. F., and Romero, V.J. Bootstrapping and Jackknife Resampling to Improve Sparse-Data UQ Methods For Tail Probability Estimates with Limited Samples, ASME V&V Verification and Validation Symposium May 2019. pdf

  7. Jekel, C.F., Haftka, R.T., Venter, G. and Venter, M.P., 2018. Lack-of-fit Tests to Indicate Material Model Improvement or Experimental Data Noise Reduction. In 2018 AIAA Non-Deterministic Approaches Conference (p. 1664). doi:10.2514/6.2018-1664 pdf

Technical reports and other non-refereed papers

  1. Jekel, C.F., Venter, M.P., Venter, G., and Haftka, R.T., 2022. Importance of Weighting Full-Field Displacement Components when Fitting Material Parameters. ResearchGate prepint doi:10.13140/RG.2.2.35039.33442 pdf

  2. Jekel, C. F., Swartz, K. E., White, D. A., Tortorelli, D. A., & Watts, S. E. 2022. Neural Network Layers for Prediction of Positive Definite Elastic Stiffness Tensors. arXiv preprint arXiv:2203.13938 pdf

  3. Jekel, C.F. and Haftka, R.T., 2021. Testing Surrogate-Based Optimization with the Fortified Branin-Hoo Extended to Four Dimensions. arXiv preprint arXiv:2107.08035 pdf

  4. Jekel, C.F., Venter, M.P., Venter, G., and Haftka, R.T., 2020. Effect of Weighting Full-field Residuals when Fitting Material Model Parameters. ResearchGate prepint doi:10.13140/RG.2.2.28328.44807 pdf

  5. Jekel, C.F. and Romero, V.J., Conservative Estimation of Tail Probabilities from Limited Sample Data. Sandia Report SAND2020-2828. March 2020. doi:10.2172/1605343 pdf

  6. Jekel, C.F. and Haftka, R.T., 2019. Fortified Test Functions for Global Optimization and the Power of Multiple Runs. arXiv preprint arXiv:1912.10575 pdf

  7. Jekel, C.F. and Venter, G., 2019. pwlf: A Python Library for Fitting 1D Continuous Piecewise Linear Functions. https://github.com/cjekel/piecewise_linear_fit_py pdf

  8. Jekel, C.F. and Haftka, R.T., 2018. Classifying Online Dating Profiles on Tinder using FaceNet Facial Embeddings. arXiv preprint arXiv:1803.04347 pdf

Software

pwlf

Star PyPI - Downloads Test coverage

similaritymeasures

Star PyPI - Downloads Test coverage

tindetheus

Star PyPI - Downloads

DTW c++

Star Test coverage

toleranceinterval

Star PyPI - Downloads Test coverage

spdlayers

Star PyPI - Downloads


Courses taught

2017: Python Programming - 1 credit hour graduate course - Syllabus - Course material

Created and taught the first Python Programming course in the MAE department at the University of Florida. The course covers topics from the basics of Python to the most popular scientific Python libraries in an effort to prepare graduate students to perform research in Python.


Honors and awards

2016: University of Florida GSFA - four year graduate school fellowship
2016: Stellenbosch departmental bursary - three year PhD funding
2016: MEng, obtained cum laude
2014: Wilhelm Frank Trust - research funding (~$50k secured with team)
2013: BSc, obtained with honor - graduated third in class
2010: Reisher Family Scholarship - three year award
2009: Braxton Technologies Scholarship - four year award


Presentations

2022: Using conservation laws to infer deep learning model accuracy of Richtmyer-Meshkov instabilities. 8th European Congress on Computational Methods in Applied Sciences and Engineering, Oslo, Norway. pdf

2022: Machine learning material models for multiscale topology optimization. Mechanics of Materials Lawrence Livermore National Lab working group. pdf

2022: Learning Richtmyer-Meshkov Instability Fields from Parametrized Hydrodynamic Simulations. JOWOG 34: Applied Computer Science. pdf

2021: Surrogate models of elastic responses from truss lattices for multiscale design. 14th World Congress of Structural and Multidisciplinary Optimization. Video pdf

2020: Surrogate Based Optimization. Overview presented for LLNL Center Design Optimization. pdf

2020: Fortifying Favorite Test Functions for Testing Global Optimization Algorithms. In 2020 AIAA AVIATION FORUM. Video

2020: Risk Allocation for Design Optimization with Unidentified Statistical Distributions. In 2020 AIAA Non-Deterministic Approaches Conference. Orlando, Florida. pdf

2019: Isotropic and orthotropic parameter identification from full field bulge inflation tests on PVC-coated polyester. The Seventh International Conference On Structural Engineering, Mechanics and Computation, Cape Town, South Africa. pdf

2019: Advances with Reviewing Personalized Tinder Profiles Using FaceNet and Historical Preference. University of Florida Data Science and Informatics Spring Symposium, Gainesville, Florida. pdf

2018: Conservative Estimation of Tail Probabilities from Limited Sample Data. University of Florida Workshop Risk Management Approaches in Engineering Applications, Gainesville, Florida. pdf

2018: Using FaceNet to automatically like Tinder profiles based on individual preference. University of Florida Data Science and Informatics Spring Symposium, Gainesville, Florida.

2018: Lack-of-fit Tests to Indicate Material Model Improvement or Experimental Data Noise Reduction. AIAA Non-Deterministic Approaches Conference, Kissimmee, Florida. pdf

2015: Obtaining Material Models for Inflatable Structures via Inverse Bubble Inflation. Stellenbosch University Mechanical & Mechatronic Engineering Department Research Colloquium, Stellenbosch, South Africa.

2015: Obtaining Material Models for use in Finite Element Analyses of PVC-coated polyester via an Inverse Bubble Inflation Method. CIMNE VII International Conference on Textile Composites and Inflatable Structures, Barcelona, Spain.

2014: An Inverse Method for Generating Polymer Properties for use in Finite Element Analyses via Bubble Inflation Testing. SAImechE Mechanical, Manufacturing, and Materials Engineering Conference, Stellenbosch, South Africa.


Professional service