Neural networks minimize Willmore energy on embedded surfaces, recovering the round sphere and Clifford torus while supplying a search procedure for genus-2 minimal surfaces.
Numerical Calabi–Yau metrics from holomorphic networks
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PINNs can address differential geometry problems by training neural networks to minimize functionals that encode geometric conditions, as shown through summaries of three related studies.
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Minimising Willmore Energy via Neural Flow
Neural networks minimize Willmore energy on embedded surfaces, recovering the round sphere and Clifford torus while supplying a search procedure for genus-2 minimal surfaces.
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PINNs in More General Geometry
PINNs can address differential geometry problems by training neural networks to minimize functionals that encode geometric conditions, as shown through summaries of three related studies.