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Found 4 papers, 2 papers with code
Learning to correct spectral methods for simulating turbulent flows
We build upon Fourier-based spectral methods, which are known to be more efficient than other numerical schemes for simulating PDEs with smooth and periodic solutions.
Variational Data Assimilation with a Learned Inverse Observation Operator
Variational data assimilation optimizes for an initial state of a dynamical system such that its evolution fits observational data.
Machine learning accelerated computational fluid dynamics
Numerical simulation of fluids plays an essential role in modeling many physical phenomena, such as weather, climate, aerodynamics and plasma physics.
Learning Memory Access Patterns
In this paper, we demonstrate the potential of deep learning to address the von Neumann bottleneck of memory performance.
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