Computational Fluid Dynamics (CFD) is essential in contemporary engineering design, yet it remains expensive to compute and can take thousands of CPU/GPU hours or more for a single high-fidelity simulation. Recent developments in machine learning for scientific modeling have led to powerful surrogate frameworks that ca...
Yi-Chen Di, Valentin Duruisseaux, Di Zhou et al.· 1 citation
The FC-PINO (Fourier-Continuation-based Physics-Informed Neural Operator) architecture is introduced, which extends the accuracy and efficiency of PINO and spectral differentiation to non-periodic and non-smooth PDEs and demonstrates that Fourier continuation is critical for extending PINO to a wider range of PDE probl...
Ha Maust, Valentin Duruisseaux, Zong-Yi Li et al.· 16 citations
Previous work built AI-based surrogates for a nonlinear gyrokinetic simulation code with the goal of using them for fast, direct calculations of turbulent ion heat flux in stellarator design optimizations and scenario planning for experiments. These AI surrogates were trained on data from \textgreater{}200k nonlinear...
R. Churchill, M. Landreman, Jong Youl Choi et al.· Plasma Physics and Controlle...· 0 citations
Many scientific questions require reasoning about what was never observed: What if the conditions, interventions, or history had been different? Models can predict accurately on observed data yet fail on such what-if queries when correlated inputs are varied independently. A common remedy is to add controlled simulatio...
Ding-Ling Yao, Kahaan Gandhi, Valentin Duruisseaux et al.· 1 citation
The results show that operator-learning surrogates can enable inverse design in quantum systems whose Hilbert spaces are too large for conventional direct optimization and enable inverse design in quantum systems whose Hilbert spaces are too large for conventional direct optimization.
A. Pipi, Valentin Duruisseaux, Emily M. Been et al.· 0 citations
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