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Pressio: Projection-based model reduction for large-scale nonlinear dynamical systems (PI)

**Goal**:** **This project aims to enable parallel, scalable, and performant projection-based model reduction capabilities to be adopted by any C++ application in a minimally intrusive manner with Pressio, an open-source C++11 header-only library.**Sandia collaborator**: Eric Parish.**External collaborators**: Francesco Rizzi (NexGen, lead developer), Mikolaj Zuzek (NexGen).**Research topics**: nonlinear model reduction; high performance computing**Funding source**: Sandia National Laboratories’ Advanced Simulation and Computing Verification and Validation program. **Project Website: **https://pressio.github.io/

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Rigorous Surrogates for Quantifying Model Uncertainty

**Goal:** This project aims to develop novel model reduction methods for nonlinear computational simulations. **Sandia collaborators:** Eric Parish (PI), Elizabeth Krath, Chi Hoang, Yuki Shimizu. **Research topics:** nonlinear model reduction; error estimation**Funding source:** Sandia National Laboratories’ Advanced Simulation and Computing Verification and Validation program.

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Full-Airframe Sensing Technology for Hypersonic Aerodynamics Measurements

**Goal:** Develop scientific machine learning approaches to infer the pressure distribution on a hypersonic flight vehicle given internal strain gauge and thermocouple readings**External collaborators (selected): **Noel Clemens (UT Austin, PI), Karen Willcox (UT Austin), Julie Pham (UT Austin), Carlos Cesnik (U Michigan)** ****Research topics: **scientific machine learning; inverse problems; model order reduction; aerothermodynamics modeling**Funding source: **AFOSR/NASA University Leadership Initiative**Project website:** https://fast.ae.utexas.edu/

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Data propagation components for the Sandia Parallel Aerodynamics and Reentry Code

**Goal:** Implement an adjoint capability in the Sandia Parallel Aerodynamics and Reentry Code in support of inverse problems and design optimization. **Sandia collaborators:** Eric Phipps (PI), Jaideep Ray, Kathryn Maupin, Denis Ridzal**Research topics:** adjoint methods; inverse problems, high performance computing**Funding source:** Sandia National Laboratories’ Advanced Simulation and Computing Advanced Technology Development and Mitigation program.