Adaptive canonicalization selects input canonical forms by maximizing network predictive confidence to yield continuous symmetry-preserving models with universal approximation for equivariant geometric networks.
Discovering symmetry breaking in physical systems with relaxed group convolution.arXiv preprint arXiv:2310.02299
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ML surrogates accurately emulate single steps of simplified thrombectomy simulations with speedups but lack stability over long times with complex geometries.
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Adaptive Canonicalization with Application to Invariant Anisotropic Geometric Networks
Adaptive canonicalization selects input canonical forms by maximizing network predictive confidence to yield continuous symmetry-preserving models with universal approximation for equivariant geometric networks.
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An Exploratory Study into using Machine-Learning for Fast Step-by-step Emulation of Numerical Mechanical Thrombectomy Simulations for Ischemic Stroke
ML surrogates accurately emulate single steps of simplified thrombectomy simulations with speedups but lack stability over long times with complex geometries.