Neural networks, structured with layers of neurons, rely on activation functions to introduce non-linearity, crucial for learning complex patterns. These functions enable networks to transcend ...

I have run a MLP Neural Network using IBM SPSS software and I got in the report summary the "parameter estimates" (the connections weights, I guess).

By embedding the mathematical structure of partial differential equations into the loss functions of neural networks, these methods achieve high accuracy in simulating complex systems—even in ...

For all the neural network models, we used a fully connected network with five hidden layers, each of which has 200 nodes, and with the leaky-rectified linear unit (ReLU) as the activation function to ...

Also, they only test linear, exponential, and modulus functions; we plan to test these along with other functions. Professor Liu has stated that neural networks are much better at interpolating than ...

However, for time independent potentials we can reduce the equation into a simpler form called Time Independent Schrödinger Equation. This notebook demonstrates how to approximate solutions to the ...

Furthermore, Zhang neural network globally exponentially converges to the exact solution of linear time-varying equations. Simulation results, including the application to robot kinematic control, ...

Solving Maxwell’s equations, a set of four fundamental equations that define electromagnetism and optics, is fundamental to every task in computational photonics. Solving these equations for modern ...