Cárdenas, Juan Manuel

Juan M. Cardenas and Alireza Doostan, Modeling unobserved variables to characterize model discrepancy. In preparation, 2025.

Audrey Gaymann, Juan M. Cardenas, and Alireza Doostan, Integration of Local and Global Surrogates for Probabilistic Failure Estimation . In preparation, 2025.

Ben Adcock, Juan M. Cardenas, Nick Dexter, A unified framework for learning with nonlinear model classes from arbitrary linear samples Proceedings of the 41st International Conference on Machine Learning, PMLR 235:169-202, 2024(2023). Preprint:  arXiv:2311.14886    

Ben Adcock, Juan M. Cardenas, Nick Dexter, CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions . Thirty-seventh Conference on Neural Information Processing Systems.  Preprint: arXiv.2306.00945 

Ben Adcock, Juan M. Cardenas, Nick Dexter, CAS4DL: Christoffel Adaptive Sampling for function approximation via Deep Learning . Sampl. Theory Signal Process. Data Anal. 20, 21 (2022).  Preprint: arXiv.2208.12190 

Ben Adcock, Juan M. Cardenas, Nick Dexter, Sebastian Moraga, The quest for optimal sampling strategies for learning sparse approximations in high dimensions. Published by  International Conference on Computational Harmonic Analysis 2021 (ICCHA-2021). Preprint: [PDF] from univie.ac.at  

Ben Adcock, Juan M. Cardenas, Nick Dexter, An Adaptive sampling and domain learning strategy for multivariate function approximation on unknown domains. SIAM J. Sci. Comput. 45(1):A200-A:225,  2023 .Preprint: arXiv:2202.00144 

B. Adcock, J. M. Cardenas, N. Dexter and S. Moraga, Towards optimal sampling for learning sparse approximations in high dimensions(preview) High Dimensional Optimization and Probability, Springer Optimization and Its Applications, Vol 191. pp 9-77. Springer, Cham. Preprint: arXiv.2202.02360 

Juan M. Cardenas and Manuel Solano.  A high order unfitted hybridizable discontinuous Galerkin method for linear elasticity Center for Research in Mathematical Engineering (CI2MA). IMA J. Numer. Anal.  2023. Preprint.  ArXiv Preprint: arXiv:2202.03410 

Ben Adcock, Juan M. Cardenas. Near-Optimal Sampling Strategies for Multivariate Function Approximation on General Domains. SIAM J. Math. Data Sci. 2(3):607-630. Preprint: arXiv:1908.01249 

B. Adcock, J. M. Cardenas, N. Dexter and S. Moraga, Towards optimal sampling for learning sparse approximations in high dimensions(preview) High Dimensional Optimization and Probability, Springer Optimization and Its Applications, Vol 191. pp 9-77. Springer, Cham.  Preprint:  arXiv:2202.02360 

“Modeling unobserved variables to model discrepancy between models” – 18th U.S. National Congress on Computational Mechanics (USNCCM). Chicago, U.S, 2025.

“Active learning strategies based on arbitrary data and modeling unobserved variables” – Coloquio DIM, Department of Engineering Mathematics, University of Concepcion (UDEC). Concepcion, Chile, 2025. 

“Active learning strategies based on arbitrary data and modeling unobserved variables” – Seminar Caleta Numerica , Mathematics Institute, Pontificia Universidad Católica de Valparaíso (PUCV). Valparaiso, Chile, 2025. 

“Modeling unobserved variables in dynamical systems” – SIAM Conference on Computational Science and Engineering (CSE25) 2025. Forth Worth, US, March 3-7, 2025 (Talk)

“An Adaptive Sampling Strategy for Multifidelity Uncertainty Quantification” – SIAM Conference on Uncertainty Quantification (UQ24). Trieste, Italy, February 27- March 1, 2024. (Talk)

“An adaptive sampling strategy to approximate partial differential equations from noisy data” – Seventh Chilean Workshop on Numerical Analysis of Partial Differential Equations (WONAPDE 2024). Concepcion, Chile, January 14-19, 2024. (Talk)

“CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions” – Thirty-seventh Annual Conference on Neural Information Processing Systems (NeurIPS 2023). New Orleans, U.S., December 10-16, 2023. (Poster)

“CAS4DL: Christoffel Adaptive Sampling for Deep Learning in Scientific Computing Applications” – SIAM Conference in Computing Science and Engineering (CSE23) – Amsterdam, Netherlands, February 26 – March 3, 2023. (Talk)

“CAS4DL: Christoffel Adaptive Sampling for Deep Learning in Data-Scarce Applications” – SIAM Conference on Mathematics of Data Science (MDS22) – San Diego, California, U.S. – September 26-30, 2022. (Talk)

“The quest for optimal sampling strategies for learning sparse approximations in high dimensions” – International Conference on Computational Harmonic Analysis – Munich, Germany – September 13-17, 2021.(Talk)

“Adaptive sampling and domain learning strategies for multivariate function approximation on known and unknown domains”  –  Canadian Applied and Industrial Mathematics Society Annual Meeting 2019 – University of Waterloo, Waterloo, Canada – June 21-24, 2019. (Talk)

“Adaptive sampling and domain learning strategies for multivariate function approximation on known and unknown domains” – SIAM Conference on Computational Science and Engineering – Forth Worth, Texasm, U.S. – March 1-5, 2021. (Poster)

“Optimal sampling of a multivariate function on a irregular domain” – SIAM Pacific North West Conference – Seattle University, Washington, U.S. – October 18-20, 2019. (Talk)

“Optimal sampling of a multivariate function on a irregular domain” – Canadian Applied and Industrial Mathematics Society Annual Meeting 2019 – Whistler Conference Center, Whistler, B.C., Canada – June 9-13, 2019. (Talk)

“Hibridized Discontinuous Galerkin method for linear elasticity in curved comains” – XXXI Jornada de Matematica de la Zona Sur – Universidad Austral de Chile, Valdivia, Chile – April 25-27, 2018.(Talk)

“Learning conditional diffusion models for uncertainty quantification problems in
Engineering, SIAM Conference on Uncertainty Quantification, Minneapolis, Minnesota, U.S.
March 22-25,2026.