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Found 3 papers, 3 papers with code
Scaling physics-informed hard constraints with mixture-of-experts
Our approach imposes the constraint over smaller decomposed domains, each of which is solved by an "expert" through differentiable optimization.
Neural Spectral Methods: Self-supervised learning in the spectral domain
In contrast to current machine learning approaches which enforce PDE constraints by minimizing the numerical quadrature of the residuals in the spatiotemporal domain, we leverage Parseval's identity and introduce a new training strategy through a \textit{spectral loss}.
AutoIP: A United Framework to Integrate Physics into Gaussian Processes
Physical modeling is critical for many modern science and engineering applications.
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