Research¶
Scalable simulations and inverse problems¶
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Extreme-Scale Inference
Global-Scale Inference and Uncertainty Quantification for Rheological Parameters in Earth's Mantle Hu, et al. (2024), Rudi, et al. (2022)
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Non-Newtonian Flow
Advanced Newton Methods and Iterative Solvers for Viscoplastic Stokes Flow Rudi, et al. (2020), Rudi, et al. (2017), Rudi, et al. (2015)
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Dynamic Mesh Refinement
Dynamic AMR-Enabled Simulations with PETSc and p4est for a Relativistic Electron Drift-Kinetic Solver Rudi, et al. (2024)
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Code Transformation
Code Transformation and Generation for Scientific Computing and HPC Rudi, et al. (2025) Lee, et al. (2025)
Solving inverse problems with neural networks¶
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Neural Networks Inference
Inverse Maps: Neural Networks for Parameter Estimation in Neuronal Dynamics and Intractable Likelihoods Rudi, et al. (2022) Lenzi, et al. (2023) Villalobos, et al. (2025)
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Training Neural Networks
Cerebras AI Accelerated Training of Inverse Maps and Subgrid-Scale Models Villalobos, et al. (2025)
