Development and testing of subcircuit surrogate models using Data-Driven Exterior Calculus and Xyce-PyMi
High level overview of Data-driven Discrete Exterior Calculus machine learning framework applied to device and circuit modeling.
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High level overview of Data-driven Discrete Exterior Calculus machine learning framework applied to device and circuit modeling.
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Presentation for USNCCM on work on non-intrusive reduced order models for aerothermal modeling.
Decisions made during early conceptual design can have a profound impact on life-cycle cost (LCC). Widely accepted that nearly 80% of LCC is committed. Decisions made during early design must be well informed. Advanced Concepts Office (ACO) at Marshall Space Flight Center aids in decision making for launch vehicles. Provides rapid turnaround pre-phase A and phase A studies. Provides customer with preliminary vehicle sizing information, vehicle feasibility, and expected performance.
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Turbulent Transport: Plays a critical role in astrophysical plasmas, such as the solar interior, spanning multiple scales and challenging traditional modeling approaches. Objective: Develop machine learning (ML) models—MLP and CNN—to predict subgrid Reynolds stress tensors from StellarBox 3D simulations of the solar atmosphere. Benchmarking: Compare ML-driven models against physics-based Gradient and Smagorinsky approaches.
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