Using finite differences instead of automatic differentiation in the loss function makes a neural kink-soliton solver about 45 percent faster than PINN with comparable accuracy.
The Nonlinear Universe: Chaos, Emergence, Life
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Comparative Study of Neural Network Methods for Solving Topological Solitons
Using finite differences instead of automatic differentiation in the loss function makes a neural kink-soliton solver about 45 percent faster than PINN with comparable accuracy.