Computational Engineering Analysis and Design (CEAD) Lab


The CEAD Lab at South Dakota Mines, created and led by PI Prashant K. Jha, aims to address challenging problems of modeling, analysis, and design in engineering fields. Broadly, we are interested in fracture and failure in materials, functional soft materials, and granular materials.


We are actively seeking enthusiastic undergraduate and graduate students interested in computational mechanics and applying advanced numerical and machine learning-based methods to problems in fracture mechanics, functional soft materials, and granular materials.

Latest Preprints

Prashant K. Jha, Patrick Diehl, Robert Lipton (2024). Nodal finite element approximation of peridynamics. CMAME.

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Prashant K. Jha (2023). Residual-based error corrector operator to enhance accuracy and reliability of neural operator surrogates of nonlinear variational boundary-value problems. CMAME.

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Prashant K. Jha, Timothy Breitzman, Kaushik Dayal (2023). Discrete-to-Continuum Limits of Long-Range Electrical Interactions in Nanostructures. ARMA.

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Lianghao Cao, Thomas O'Leary-Roseberry, Prashant K. Jha, J. Tinsley Oden, Omar Ghattas (2023). Residual-Based Error Correction for Neural Operator Accelerated Infinite-Dimensional Bayesian Inverse Problems. JCP.

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Prashant K. Jha, J. Tinsley Oden (2022). Goal-Oriented A-Posteriori Estimation of Model Error as an Aid to Parameter Estimation. JCP.

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Latest News

Feb 26, 2025 1 min read
Ian was selected for 2025 REU Summary Research Position award to work on design of functional materials

Congratulations to Ian. This award will allow Ian to work on the design of functional materials during the summer 2025.
Oct 5, 2021 1 min read
Virtual Thematic Conference (January 10 - 11, 2022) on Computational Oncology

With colleagues, Ernesto Lima and Chengyue Wu, at Oden Institute, we are organizing a virtual thematic conference on computational oncology. The event is supported by Biological Systems TTA in USACM. We have a greate lineup of speakers and very excited to bring experts in one platform to discuss the modeling and data related issues in modeling cancer.