Holomorphic neural networks enforce exact satisfaction of harmonic PDEs for 3D Laplace and elasticity problems using Whittaker representations and boundary-only training.
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Domain experts require fast convergence and some explainability from evolutionary algorithms in physics-informed optimization, with other needs varying by problem, revealing an application gap.
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Performance and Explainability Requirements of Evolutionary Algorithms in Real-World Physics-Informed Optimization
Domain experts require fast convergence and some explainability from evolutionary algorithms in physics-informed optimization, with other needs varying by problem, revealing an application gap.