MDOcean is a multidisciplinary semi-analytical model for wave energy converters that runs in 151 ms with near-numerical accuracy for techno-economic analysis.
Karthik Duraisamy, Gianluca Iaccarino, and Heng Xiao
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A data-driven framework learns a unified, frame-invariant turbulence model from sparse observations across regimes via multi-objective ensemble learning and similarity-based case selection.
PPE is a novel predictor-corrector method for interactive Pareto set exploration in deep multi-task learning that approximates tangent spaces via Krylov subspace iterations using only matrix-vector products from automatic differentiation.
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Development, Validation, and Benchmarking of a Multidisciplinary Semi-Analytical Model for Wave Energy Converters
MDOcean is a multidisciplinary semi-analytical model for wave energy converters that runs in 151 ms with near-numerical accuracy for techno-economic analysis.
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Toward a unified data-driven turbulence model through multi-objective learning
A data-driven framework learns a unified, frame-invariant turbulence model from sparse observations across regimes via multi-objective ensemble learning and similarity-based case selection.
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Interactive Pareto navigation for deep multi-task learning
PPE is a novel predictor-corrector method for interactive Pareto set exploration in deep multi-task learning that approximates tangent spaces via Krylov subspace iterations using only matrix-vector products from automatic differentiation.